Part 3. Extracting signal from a region of interest answers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 3. Extracting signal from a region of interest\n",
    "\n",
    "In Part 1, we preprocessed the data (or, at least, explored *how* you could go about preprocessing your data). Let's just use the `fmriprep`-preprocessed data for the remainder of the tutorial. Here, we will extract signal from a region of interest: striatum. Striatum is part of the Basal Ganglia, and has repeatedly been implicated in response caution adjustments (e.g., [Forstmann et al 2008](http://www.pnas.org/content/105/45/17538/#F2); [Van Maanen et al 2011](http://www.jneurosci.org/content/31/48/17488)). In Part 3 and 4 of this tutorial, we'll see if we can replicate this result.\n",
    "\n",
    "In order to extract signal from a region of interest, we need a mask that tells us where this ROI lies in the brain. Since `fmriprep` registered ('normalized') our data to MNI152-space, we can use a mask from an atlas in MNI152-space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import nibabel as nib\n",
    "from nipype.interfaces import fsl\n",
    "import nilearn.datasets\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A look at masks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the directory `/data/masks/` you can find various masks, including one for the left and one for the right striatum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPe_L.nii.gz  GPi_L.nii.gz  STR_L.nii.gz\n",
      "GPe_R.nii.gz  GPi_R.nii.gz  STR_R.nii.gz\n"
     ]
    }
   ],
   "source": [
    "!ls /data/masks/MNI152-2mm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load a mask and see what it looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7efc5f87bb20>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "str_l = nib.load('/data/masks/MNI152-2mm/STR_L.nii.gz').get_fdata()\n",
    "plt.imshow(str_l[:, 68, :].T, origin='lower', cmap=plt.cm.hot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this is a *probabilistic* map. The colors in the above plot indicate the *probability* for each voxel that is is part of any given individual's left striatum. Further, probabilities of 0 lead to a black pixel in the plot above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's mask all voxels that have a probability of 50% or less of belonging to any of the striatal parts. This way, they won't end up as black pixels when we plot them, but become transparant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "str_l = np.ma.masked_less(str_l, 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, `str_l` is a 3D-matrix indicating for each voxel the probability that it belongs to the left striatum. What does this look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7efc5f797670>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(str_l[:, 68, :].T, origin='lower', cmap=plt.cm.hot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The background is now white instead of black, which doesn't help much. But now we can superimpose this mask on an MNI152 template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the MNI template\n",
    "mni = nib.load('/data/mni_icbm152_nlin_asym_09c/2mm_T1.nii.gz').get_fdata()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "\n",
    "plt.subplot(131)\n",
    "plt.imshow(mni[:, 68, :].T,origin='lower', cmap=plt.cm.gray)\n",
    "plt.imshow(str_l[:, 68, :].T,origin='lower', cmap=plt.cm.hot)\n",
    "plt.grid('off')\n",
    "\n",
    "plt.subplot(132)\n",
    "plt.imshow(mni[38, :, :].T,origin='lower', cmap=plt.cm.gray)\n",
    "plt.imshow(str_l[38, :, :].T,origin='lower', cmap=plt.cm.hot)\n",
    "plt.grid('off')\n",
    "\n",
    "plt.subplot(133)\n",
    "plt.imshow(mni[:, :, 40].T,origin='lower', cmap=plt.cm.gray)\n",
    "plt.imshow(str_l[:, :, 40].T,origin='lower', cmap=plt.cm.hot)\n",
    "plt.grid('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And that's where left striatum is located probabilistically"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the `str_l` variable is a kind of image. You can apply image processing tools to it; for example, to find the center of mass (this is how the slices plotting above were chosen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38.03372758952017, 68.37898108274199, 40.79747377169487)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy as sp\n",
    "from scipy import ndimage\n",
    "com = ndimage.center_of_mass(str_l)\n",
    "com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Can you make the same plots for right striatum? Find the center of mass, and plot it on an MNI152-brain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "str_r = nib.load('/data/masks/MNI152-2mm/STR_R.nii.gz').get_fdata()\n",
    "str_r = np.ma.masked_less(str_r, 0.5)\n",
    "com = ndimage.center_of_mass(str_r)\n",
    "\n",
    "plt.figure(figsize=(20, 10))\n",
    "\n",
    "plt.subplot(131)\n",
    "plt.imshow(mni[:, int(com[1]), :].T,origin='lower', cmap=plt.cm.gray)\n",
    "plt.imshow(str_r[:, int(com[1]), :].T,origin='lower', cmap=plt.cm.hot)\n",
    "plt.grid('off')\n",
    "\n",
    "plt.subplot(132)\n",
    "plt.imshow(mni[int(com[0]), :, :].T,origin='lower', cmap=plt.cm.gray)\n",
    "plt.imshow(str_r[int(com[0]), :, :].T,origin='lower', cmap=plt.cm.hot)\n",
    "plt.grid('off')\n",
    "\n",
    "plt.subplot(133)\n",
    "plt.imshow(mni[:, :, int(com[2])].T,origin='lower', cmap=plt.cm.gray)\n",
    "plt.imshow(str_r[:, :, int(com[2])].T,origin='lower', cmap=plt.cm.hot)\n",
    "plt.grid('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting signal from a masked region\n",
    "Back to Nipype. Nipype contains interfaces that allows you to extract a time series *meaned* over a region of interest. The interface is called `ImageMeants()`. Let's first see how this works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We'll need these libraries below\n",
    "from nipype.interfaces import fsl\n",
    "import nipype.pipeline.engine as pe\n",
    "import nipype.interfaces.io as nio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub-483_task-SAT_run-1_desc-confounds_regressors.json\n",
      "sub-483_task-SAT_run-1_desc-confounds_regressors.tsv\n",
      "sub-483_task-SAT_run-1_space-MNI152NLin2009cAsym_boldref.nii.gz\n",
      "sub-483_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-aparcaseg_dseg.nii.gz\n",
      "sub-483_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-aseg_dseg.nii.gz\n",
      "sub-483_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-brain_mask.json\n",
      "sub-483_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz\n",
      "sub-483_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold.json\n",
      "sub-483_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz\n",
      "sub-483_task-SAT_run-2_desc-confounds_regressors.json\n",
      "sub-483_task-SAT_run-2_desc-confounds_regressors.tsv\n",
      "sub-483_task-SAT_run-2_space-MNI152NLin2009cAsym_boldref.nii.gz\n",
      "sub-483_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-aparcaseg_dseg.nii.gz\n",
      "sub-483_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-aseg_dseg.nii.gz\n",
      "sub-483_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-brain_mask.json\n",
      "sub-483_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz\n",
      "sub-483_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold.json\n",
      "sub-483_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz\n"
     ]
    }
   ],
   "source": [
    "!ls /data/deriv/fmriprep/sub-483/func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "extracter = fsl.ImageMeants()\n",
    "extracter.inputs.in_file = '/data/deriv/fmriprep/sub-483/func/sub-483_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'\n",
    "extracter.inputs.mask = '/data/masks/MNI152-epi/STR_L.nii.gz'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "out_file = /home/neuro/model_based_fmri/sub-483_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = extracter.run()\n",
    "r.outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, using `np.loadtxt(<path_to_txt_file>)`, you can load the output. \n",
    "\n",
    "2) Can you plot the data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7efc0aded310>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw_str_data = np.loadtxt(r.outputs.out_file)   # here we load\n",
    "plt.plot(raw_str_data) # plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making a pipeline\n",
    "Now we semi-manually extracted a single subject's time series signal meaned over striatum. We could do this in the same way for all subjects, but it's much more efficient to make a pipeline. Let's make one that extracts the time series meaned over striatum for all participants. First, set-up the nodes we want to use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the node that extracts signal\n",
    "extracter = pe.MapNode(fsl.ImageMeants(), iterfield='in_file', name='extract_mean_ts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this is a [MapNode](https://pythonhosted.org/nipype/api/generated/nipype.pipeline.engine.nodes.html#mapnode) rather than a regular Node; i.e., a Node the receives a list of inputs and applies the same interface to each element of the list.\n",
    "\n",
    "3) In the cell below, create/fill: \n",
    "- the template for where to find the masks\n",
    "- the template for where to find the functional data (similar to Part 1)\n",
    "- all subject ids at selector.iterator. \n",
    "- fill in the mask names at selector.iterator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here, we set-up a SelectFile-Node\n",
    "templates = {\n",
    "    'mask': '/data/masks/MNI152-epi/{mask}.nii.gz',  # NB: use folder \"MNI152-epi\" here instead of \"MNI152-2mm\"\n",
    "    'func': '/data/deriv/fmriprep/sub-{subject_id}/func/sub-{subject_id}_task-SAT_run-*_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'\n",
    "}\n",
    "\n",
    "selector = pe.Node(nio.SelectFiles(templates=templates), name='file_selector')\n",
    "selector.iterables = [('subject_id', ['197', '372', '381', '471', '483', '498', '523', '544', '548', '549', '551', '552', '553', '554', '555', '558', '559']), \n",
    "                      ('mask', ['STR_L', 'STR_R'])]\n",
    "\n",
    "\n",
    "# A datasink is useful for exporting. Remember the base_directory - this is where the data will be saved later\n",
    "ds = pe.Node(nio.DataSink(base_directory='/data/extracted_signals'), \n",
    "             name='datasink')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, combine everything in a Workflow.\n",
    "\n",
    "4) In the cell below, make the right connections in the workflow: you want to connect the `selector` to the `extractor` in two ways. Remember that you can call `fsl.ImageMeants.help()` to see the input argument names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240725-08:57:16,778 nipype.workflow INFO:\n",
      "\t Generated workflow graph: /data/workflow_folders/extract_striatum_signal/graph.png (graph2use=hierarchical, simple_form=True).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "workflow = pe.Workflow(base_dir='/data/workflow_folders/', \n",
    "                       name='extract_striatum_signal')\n",
    "\n",
    "# Connect the Nodes\n",
    "workflow.connect(selector, 'mask', extracter, 'mask')\n",
    "workflow.connect(selector, 'func', extracter, 'in_file')\n",
    "workflow.connect(extracter, 'out_file', ds, 'extracted_timeseries')\n",
    "\n",
    "# Make a graph to visualize what we did..\n",
    "workflow.write_graph('graph.dot', format='png')\n",
    "from IPython.display import Image\n",
    "Image('/data/workflow_folders/extract_striatum_signal/graph.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we're ready to run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240725-08:57:16,855 nipype.workflow INFO:\n",
      "\t Workflow extract_striatum_signal settings: ['check', 'execution', 'logging', 'monitoring']\n",
      "240725-08:57:16,974 nipype.workflow INFO:\n",
      "\t Running in parallel.\n",
      "240725-08:57:16,978 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 0 tasks, and 34 jobs ready. Free memory (GB): 453.15/453.15, Free processors: 24/24.\n",
      "240725-08:57:17,170 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_197/file_selector\".\n",
      "240725-08:57:17,171 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_372/file_selector\".\n",
      "240725-08:57:17,171 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_197/file_selector\".\n",
      "240725-08:57:17,173 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_381/file_selector\".\n",
      "240725-08:57:17,173 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_372/file_selector\".\n",
      "240725-08:57:17,173 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_471/file_selector\".\n",
      "240725-08:57:17,173 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_381/file_selector\".\n",
      "240725-08:57:17,174 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_483/file_selector\".\n",
      "240725-08:57:17,176 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,176 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,176 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,175 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_483/file_selector\".\n",
      "240725-08:57:17,176 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_498/file_selector\".\n",
      "240725-08:57:17,175 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_498/file_selector\".\n",
      "240725-08:57:17,176 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_523/file_selector\".\n",
      "240725-08:57:17,176 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_544/file_selector\".\n",
      "240725-08:57:17,176 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_471/file_selector\".\n",
      "240725-08:57:17,177 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_544/file_selector\".\n",
      "240725-08:57:17,176 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_523/file_selector\".\n",
      "240725-08:57:17,177 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_548/file_selector\".\n",
      "240725-08:57:17,177 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_548/file_selector\".\n",
      "240725-08:57:17,177 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_549/file_selector\".\n",
      "240725-08:57:17,177 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_549/file_selector\".\n",
      "240725-08:57:17,178 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_551/file_selector\".\n",
      "240725-08:57:17,179 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001179s.\n",
      "240725-08:57:17,179 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_552/file_selector\".\n",
      "240725-08:57:17,179 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_551/file_selector\".\n",
      "240725-08:57:17,180 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001482s.\n",
      "240725-08:57:17,179 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_552/file_selector\".\n",
      "240725-08:57:17,182 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001142s.\n",
      "240725-08:57:17,193 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,193 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,193 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,194 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,194 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,194 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,194 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,194 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,194 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,195 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,195 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,195 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,195 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,195 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,196 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,195 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,196 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001078s.\n",
      "240725-08:57:17,196 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,196 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,196 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,197 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001068s.\n",
      "240725-08:57:17,197 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,196 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:17,198 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001095s.\n",
      "240725-08:57:17,197 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001042s.\n",
      "240725-08:57:17,199 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001122s.\n",
      "240725-08:57:17,197 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001101s.\n",
      "240725-08:57:17,199 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001423s.\n",
      "240725-08:57:17,236 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001357s.\n",
      "240725-08:57:17,197 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001207s.\n",
      "240725-08:57:17,236 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001133s.\n",
      "240725-08:57:17,236 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001383s.\n",
      "240725-08:57:17,236 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001223s.\n",
      "240725-08:57:17,236 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001358s.\n",
      "240725-08:57:17,237 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001382s.\n",
      "240725-08:57:17,236 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001215s.\n",
      "240725-08:57:17,196 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001216s.\n",
      "240725-08:57:17,237 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001116s.\n",
      "240725-08:57:17,237 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001333s.\n",
      "240725-08:57:17,237 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001036s.\n",
      "240725-08:57:17,238 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001056s.\n",
      "240725-08:57:17,238 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.001112s.\n",
      "240725-08:57:18,980 nipype.workflow INFO:\n",
      "\t [Job 0] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:18,983 nipype.workflow INFO:\n",
      "\t [Job 1] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:18,983 nipype.workflow INFO:\n",
      "\t [Job 2] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:18,984 nipype.workflow INFO:\n",
      "\t [Job 3] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:18,985 nipype.workflow INFO:\n",
      "\t [Job 4] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:18,986 nipype.workflow INFO:\n",
      "\t [Job 5] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:18,987 nipype.workflow INFO:\n",
      "\t [Job 6] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:18,988 nipype.workflow INFO:\n",
      "\t [Job 7] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:18,988 nipype.workflow INFO:\n",
      "\t [Job 8] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:18,989 nipype.workflow INFO:\n",
      "\t [Job 9] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:18,990 nipype.workflow INFO:\n",
      "\t [Job 10] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:18,991 nipype.workflow INFO:\n",
      "\t [Job 11] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:18,991 nipype.workflow INFO:\n",
      "\t [Job 12] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:18,992 nipype.workflow INFO:\n",
      "\t [Job 13] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:18,993 nipype.workflow INFO:\n",
      "\t [Job 14] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:18,995 nipype.workflow INFO:\n",
      "\t [Job 15] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:18,996 nipype.workflow INFO:\n",
      "\t [Job 16] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:18,996 nipype.workflow INFO:\n",
      "\t [Job 17] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:18,997 nipype.workflow INFO:\n",
      "\t [Job 18] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:18,998 nipype.workflow INFO:\n",
      "\t [Job 19] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:18,999 nipype.workflow INFO:\n",
      "\t [Job 20] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:18,999 nipype.workflow INFO:\n",
      "\t [Job 21] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:19,0 nipype.workflow INFO:\n",
      "\t [Job 22] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:19,1 nipype.workflow INFO:\n",
      "\t [Job 23] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:19,2 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 0 tasks, and 34 jobs ready. Free memory (GB): 453.15/453.15, Free processors: 24/24.\n",
      "240725-08:57:19,100 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_553/file_selector\".\n",
      "240725-08:57:19,100 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_553/file_selector\".\n",
      "240725-08:57:19,100 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_554/file_selector\".\n",
      "240725-08:57:19,102 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_554/file_selector\".\n",
      "240725-08:57:19,102 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_555/file_selector\".\n",
      "240725-08:57:19,102 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_555/file_selector\".\n",
      "240725-08:57:19,103 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_558/file_selector\".\n",
      "240725-08:57:19,103 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_558/file_selector\".\n",
      "240725-08:57:19,104 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_559/file_selector\".\n",
      "240725-08:57:19,105 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.file_selector\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_559/file_selector\".\n",
      "240725-08:57:19,120 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:19,120 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:19,120 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:19,120 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:19,120 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:19,120 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:19,121 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:19,121 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:19,121 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:19,121 nipype.workflow INFO:\n",
      "\t [Node] Executing \"file_selector\" <nipype.interfaces.io.SelectFiles>\n",
      "240725-08:57:19,122 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.000582s.\n",
      "240725-08:57:19,122 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.00063s.\n",
      "240725-08:57:19,122 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.000595s.\n",
      "240725-08:57:19,122 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.000615s.\n",
      "240725-08:57:19,123 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.000794s.\n",
      "240725-08:57:19,123 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.000578s.\n",
      "240725-08:57:19,123 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.000582s.\n",
      "240725-08:57:19,123 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.000585s.\n",
      "240725-08:57:19,123 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.000593s.\n",
      "240725-08:57:19,123 nipype.workflow INFO:\n",
      "\t [Node] Finished \"file_selector\", elapsed time 0.000666s.\n",
      "240725-08:57:20,982 nipype.workflow INFO:\n",
      "\t [Job 24] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:20,983 nipype.workflow INFO:\n",
      "\t [Job 25] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:20,983 nipype.workflow INFO:\n",
      "\t [Job 26] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:20,984 nipype.workflow INFO:\n",
      "\t [Job 27] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:20,985 nipype.workflow INFO:\n",
      "\t [Job 28] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:20,986 nipype.workflow INFO:\n",
      "\t [Job 29] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:20,986 nipype.workflow INFO:\n",
      "\t [Job 30] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:20,987 nipype.workflow INFO:\n",
      "\t [Job 31] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:20,988 nipype.workflow INFO:\n",
      "\t [Job 32] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:20,988 nipype.workflow INFO:\n",
      "\t [Job 33] Completed (extract_striatum_signal.file_selector).\n",
      "240725-08:57:20,989 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 0 tasks, and 58 jobs ready. Free memory (GB): 453.15/453.15, Free processors: 24/24.\n",
      "240725-08:57:21,87 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_197/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:21,87 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_197/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:21,87 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_197/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:21,89 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_197/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:21,89 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,90 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,90 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,90 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_372/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:21,91 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,91 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_372/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:21,93 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,93 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_372/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:21,93 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_372/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:21,94 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_381/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:21,95 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,95 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,95 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_381/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:21,97 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,97 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,98 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,98 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_381/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:21,100 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,106 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_381/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:21,107 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_471/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:21,108 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_471/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:21,108 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,109 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,110 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,114 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_471/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:21,114 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_471/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:21,115 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_483/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:21,115 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_483/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:21,116 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_483/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:21,116 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_483/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:21,117 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,116 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_498/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:21,117 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,117 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,118 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,119 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,119 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_498/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:21,119 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,119 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_498/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:21,119 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,117 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_498/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:21,121 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,123 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:21,123 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:22,983 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 24 tasks, and 44 jobs ready. Free memory (GB): 448.35/453.15, Free processors: 0/24.\n",
      "                     Currently running:\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "240725-08:57:23,878 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 2.756962s.\n",
      "240725-08:57:23,899 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 2.779346s.\n",
      "240725-08:57:24,984 nipype.workflow INFO:\n",
      "\t [Job 118] Completed (_extract_mean_ts0).\n",
      "240725-08:57:24,985 nipype.workflow INFO:\n",
      "\t [Job 120] Completed (_extract_mean_ts0).\n",
      "240725-08:57:24,987 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 22 tasks, and 44 jobs ready. Free memory (GB): 448.75/453.15, Free processors: 2/24.\n",
      "                     Currently running:\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "240725-08:57:25,74 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_523/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:25,77 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:25,70 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_523/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:25,91 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:25,772 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 4.674027s.\n",
      "240725-08:57:25,772 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 4.650698s.\n",
      "240725-08:57:25,775 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 4.65177s.\n",
      "240725-08:57:25,759 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 4.65686s.\n",
      "240725-08:57:25,786 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 4.664977s.\n",
      "240725-08:57:25,796 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 4.699215s.\n",
      "240725-08:57:25,808 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 4.7144s.\n",
      "240725-08:57:25,901 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 4.809593s.\n",
      "240725-08:57:25,911 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 4.791171s.\n",
      "240725-08:57:25,918 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 4.827213s.\n",
      "240725-08:57:25,974 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 4.864606s.\n",
      "240725-08:57:25,988 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 4.889175s.\n",
      "240725-08:57:26,30 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 4.911677s.\n",
      "240725-08:57:26,31 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 4.920636s.\n",
      "240725-08:57:26,89 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 4.991891s.\n",
      "240725-08:57:26,101 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 4.974397s.\n",
      "240725-08:57:26,101 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 4.989384s.\n",
      "240725-08:57:26,101 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 4.982732s.\n",
      "240725-08:57:26,103 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 4.976445s.\n",
      "240725-08:57:26,112 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 5.01311s.\n",
      "240725-08:57:26,148 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 5.055621s.\n",
      "240725-08:57:26,240 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 5.148723s.\n",
      "240725-08:57:26,986 nipype.workflow INFO:\n",
      "\t [Job 102] Completed (_extract_mean_ts0).\n",
      "240725-08:57:26,987 nipype.workflow INFO:\n",
      "\t [Job 103] Completed (_extract_mean_ts1).\n",
      "240725-08:57:26,988 nipype.workflow INFO:\n",
      "\t [Job 104] Completed (_extract_mean_ts0).\n",
      "240725-08:57:26,989 nipype.workflow INFO:\n",
      "\t [Job 105] Completed (_extract_mean_ts1).\n",
      "240725-08:57:26,989 nipype.workflow INFO:\n",
      "\t [Job 106] Completed (_extract_mean_ts0).\n",
      "240725-08:57:26,990 nipype.workflow INFO:\n",
      "\t [Job 107] Completed (_extract_mean_ts1).\n",
      "240725-08:57:26,990 nipype.workflow INFO:\n",
      "\t [Job 108] Completed (_extract_mean_ts0).\n",
      "240725-08:57:26,991 nipype.workflow INFO:\n",
      "\t [Job 109] Completed (_extract_mean_ts1).\n",
      "240725-08:57:26,991 nipype.workflow INFO:\n",
      "\t [Job 110] Completed (_extract_mean_ts0).\n",
      "240725-08:57:26,992 nipype.workflow INFO:\n",
      "\t [Job 111] Completed (_extract_mean_ts1).\n",
      "240725-08:57:26,993 nipype.workflow INFO:\n",
      "\t [Job 112] Completed (_extract_mean_ts0).\n",
      "240725-08:57:26,993 nipype.workflow INFO:\n",
      "\t [Job 113] Completed (_extract_mean_ts1).\n",
      "240725-08:57:26,994 nipype.workflow INFO:\n",
      "\t [Job 114] Completed (_extract_mean_ts0).\n",
      "240725-08:57:26,994 nipype.workflow INFO:\n",
      "\t [Job 115] Completed (_extract_mean_ts1).\n",
      "240725-08:57:26,995 nipype.workflow INFO:\n",
      "\t [Job 116] Completed (_extract_mean_ts0).\n",
      "240725-08:57:26,995 nipype.workflow INFO:\n",
      "\t [Job 117] Completed (_extract_mean_ts1).\n",
      "240725-08:57:26,996 nipype.workflow INFO:\n",
      "\t [Job 119] Completed (_extract_mean_ts1).\n",
      "240725-08:57:26,996 nipype.workflow INFO:\n",
      "\t [Job 121] Completed (_extract_mean_ts1).\n",
      "240725-08:57:26,997 nipype.workflow INFO:\n",
      "\t [Job 122] Completed (_extract_mean_ts0).\n",
      "240725-08:57:26,997 nipype.workflow INFO:\n",
      "\t [Job 123] Completed (_extract_mean_ts1).\n",
      "240725-08:57:26,998 nipype.workflow INFO:\n",
      "\t [Job 124] Completed (_extract_mean_ts0).\n",
      "240725-08:57:26,998 nipype.workflow INFO:\n",
      "\t [Job 125] Completed (_extract_mean_ts1).\n",
      "240725-08:57:27,0 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 2 tasks, and 54 jobs ready. Free memory (GB): 452.75/453.15, Free processors: 22/24.\n",
      "                     Currently running:\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "240725-08:57:27,70 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_197/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:27,72 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_197/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:27,72 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:27,73 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_372/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:27,73 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:27,74 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:27,74 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:27,75 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:27,74 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_372/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:27,74 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:27,75 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_197/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:27,76 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_197/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:27,76 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:27,76 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:27,76 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_372/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:27,77 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:27,77 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:27,77 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:27,78 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:27,78 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:27,78 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:27,78 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_381/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:27,78 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_372/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:27,79 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:27,80 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:27,80 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:27,80 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:27,81 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_381/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:27,81 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_381/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:27,82 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_471/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:27,82 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:27,83 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:27,83 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:27,83 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:27,83 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:27,84 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_381/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:27,84 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:27,86 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:27,86 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_471/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:27,86 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_471/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:27,86 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:27,87 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:27,88 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:27,88 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:27,89 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:27,89 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_471/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:27,90 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:27,91 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:27,91 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_483/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:27,93 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:27,94 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:27,95 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_483/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:27,95 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_483/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:27,95 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_523/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:27,96 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:27,96 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:27,97 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:27,97 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:27,98 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:27,97 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_498/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:27,98 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_498/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:27,98 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_483/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:27,99 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_523/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:27,99 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:27,100 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:27,100 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:27,100 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:27,100 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:27,100 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:27,101 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_544/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:27,101 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_498/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:27,101 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:27,102 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_498/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:27,102 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_544/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:27,102 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:27,103 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:27,103 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:27,103 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:27,104 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:27,104 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:27,106 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_544/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:27,107 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_544/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:27,108 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:27,109 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:27,111 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_548/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:27,111 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_548/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:27,111 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_548/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:27,113 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:27,113 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:27,113 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:27,118 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_548/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:27,120 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:27,976 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 2.897976s.\n",
      "240725-08:57:27,990 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 2.897016s.\n",
      "240725-08:57:28,988 nipype.workflow INFO:\n",
      "\t [Job 126] Completed (_extract_mean_ts0).\n",
      "240725-08:57:28,989 nipype.workflow INFO:\n",
      "\t [Job 127] Completed (_extract_mean_ts1).\n",
      "240725-08:57:28,990 nipype.workflow INFO:\n",
      "\t [Job 34] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:28,991 nipype.workflow INFO:\n",
      "\t [Job 35] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:28,992 nipype.workflow INFO:\n",
      "\t [Job 36] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:28,992 nipype.workflow INFO:\n",
      "\t [Job 37] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:28,993 nipype.workflow INFO:\n",
      "\t [Job 38] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:28,994 nipype.workflow INFO:\n",
      "\t [Job 39] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:28,995 nipype.workflow INFO:\n",
      "\t [Job 40] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:28,996 nipype.workflow INFO:\n",
      "\t [Job 41] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:28,997 nipype.workflow INFO:\n",
      "\t [Job 42] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:28,998 nipype.workflow INFO:\n",
      "\t [Job 43] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:28,998 nipype.workflow INFO:\n",
      "\t [Job 44] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:28,999 nipype.workflow INFO:\n",
      "\t [Job 45] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:29,1 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 10 tasks, and 45 jobs ready. Free memory (GB): 451.15/453.15, Free processors: 14/24.\n",
      "                     Currently running:\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "240725-08:57:29,77 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_197/datasink\".\n",
      "240725-08:57:29,82 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:29,79 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_197/datasink\".\n",
      "240725-08:57:29,80 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_523/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:29,80 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_372/datasink\".\n",
      "240725-08:57:29,81 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:29,82 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:29,84 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:29,84 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_523/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:29,85 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:29,85 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:29,85 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_372/datasink\".\n",
      "240725-08:57:29,86 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:29,87 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_381/datasink\".\n",
      "240725-08:57:29,88 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:29,90 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:29,93 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_381/datasink\".\n",
      "240725-08:57:29,93 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_471/datasink\".\n",
      "240725-08:57:29,94 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_483/datasink\".\n",
      "240725-08:57:29,94 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_471/datasink\".\n",
      "240725-08:57:29,95 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_483/datasink\".\n",
      "240725-08:57:29,96 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_498/datasink\".\n",
      "240725-08:57:29,96 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_498/datasink\".\n",
      "240725-08:57:29,96 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_549/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:29,97 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:29,98 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:29,98 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:29,98 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:29,99 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:29,100 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:29,102 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.017219s.\n",
      "240725-08:57:29,102 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.002852s.\n",
      "240725-08:57:29,102 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:29,102 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:29,105 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.013932s.\n",
      "240725-08:57:29,112 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.028768s.\n",
      "240725-08:57:29,112 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.021203s.\n",
      "240725-08:57:29,113 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.026921s.\n",
      "240725-08:57:29,113 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.01518s.\n",
      "240725-08:57:29,118 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.015345s.\n",
      "240725-08:57:29,126 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.027049s.\n",
      "240725-08:57:29,128 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.024101s.\n",
      "240725-08:57:29,128 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.023683s.\n",
      "240725-08:57:29,129 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.028373s.\n",
      "240725-08:57:29,820 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 2.721528s.\n",
      "240725-08:57:29,847 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 2.743962s.\n",
      "240725-08:57:30,75 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 2.970106s.\n",
      "240725-08:57:30,77 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 2.971008s.\n",
      "240725-08:57:30,78 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 2.967799s.\n",
      "240725-08:57:30,109 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 2.987593s.\n",
      "240725-08:57:30,112 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 2.997416s.\n",
      "240725-08:57:30,128 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 3.018528s.\n",
      "240725-08:57:30,166 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 3.051241s.\n",
      "240725-08:57:30,180 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 3.065016s.\n",
      "240725-08:57:30,990 nipype.workflow INFO:\n",
      "\t [Job 128] Completed (_extract_mean_ts0).\n",
      "240725-08:57:30,991 nipype.workflow INFO:\n",
      "\t [Job 129] Completed (_extract_mean_ts1).\n",
      "240725-08:57:30,992 nipype.workflow INFO:\n",
      "\t [Job 130] Completed (_extract_mean_ts0).\n",
      "240725-08:57:30,992 nipype.workflow INFO:\n",
      "\t [Job 131] Completed (_extract_mean_ts1).\n",
      "240725-08:57:30,993 nipype.workflow INFO:\n",
      "\t [Job 132] Completed (_extract_mean_ts0).\n",
      "240725-08:57:30,993 nipype.workflow INFO:\n",
      "\t [Job 133] Completed (_extract_mean_ts1).\n",
      "240725-08:57:30,994 nipype.workflow INFO:\n",
      "\t [Job 134] Completed (_extract_mean_ts0).\n",
      "240725-08:57:30,994 nipype.workflow INFO:\n",
      "\t [Job 135] Completed (_extract_mean_ts1).\n",
      "240725-08:57:30,995 nipype.workflow INFO:\n",
      "\t [Job 136] Completed (_extract_mean_ts0).\n",
      "240725-08:57:30,995 nipype.workflow INFO:\n",
      "\t [Job 137] Completed (_extract_mean_ts1).\n",
      "240725-08:57:30,996 nipype.workflow INFO:\n",
      "\t [Job 46] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:30,996 nipype.workflow INFO:\n",
      "\t [Job 68] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:30,997 nipype.workflow INFO:\n",
      "\t [Job 69] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:30,998 nipype.workflow INFO:\n",
      "\t [Job 70] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:30,998 nipype.workflow INFO:\n",
      "\t [Job 71] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:30,999 nipype.workflow INFO:\n",
      "\t [Job 72] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:31,0 nipype.workflow INFO:\n",
      "\t [Job 73] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:31,0 nipype.workflow INFO:\n",
      "\t [Job 74] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:31,1 nipype.workflow INFO:\n",
      "\t [Job 75] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:31,2 nipype.workflow INFO:\n",
      "\t [Job 76] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:31,2 nipype.workflow INFO:\n",
      "\t [Job 77] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:31,3 nipype.workflow INFO:\n",
      "\t [Job 78] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:31,4 nipype.workflow INFO:\n",
      "\t [Job 79] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:31,5 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 1 tasks, and 37 jobs ready. Free memory (GB): 452.95/453.15, Free processors: 23/24.\n",
      "                     Currently running:\n",
      "                       * _extract_mean_ts0\n",
      "240725-08:57:31,124 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_523/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:31,125 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:31,125 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_544/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:31,126 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:31,126 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_544/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:31,126 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:31,127 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_523/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:31,127 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:31,128 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:31,128 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_548/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:31,128 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:31,128 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_544/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:31,128 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:31,129 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:31,129 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_523/datasink\".\n",
      "240725-08:57:31,130 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_544/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:31,130 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:31,130 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:31,130 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_548/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:31,131 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:31,131 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:31,132 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:31,132 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:31,132 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_548/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:31,132 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:31,131 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_549/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:31,132 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_549/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:31,132 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:31,133 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_549/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:31,133 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:31,133 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:31,134 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_551/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:31,134 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:31,134 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:31,134 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_548/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:31,135 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.00127s.\n",
      "240725-08:57:31,135 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:31,135 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:31,135 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_551/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:31,136 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:31,136 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:31,136 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:31,136 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_551/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:31,137 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_551/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:31,137 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:31,138 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_552/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:31,138 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_552/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:31,138 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:31,139 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:31,140 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_552/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:31,140 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:31,140 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_552/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:31,140 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:31,142 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:31,141 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_553/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:31,142 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_553/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:31,142 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_553/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:31,142 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:31,143 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_553/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:31,143 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_554/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:31,144 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:31,144 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_554/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:31,144 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:31,144 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:31,145 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:31,145 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:31,146 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:31,930 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 2.830472s.\n",
      "240725-08:57:32,992 nipype.workflow INFO:\n",
      "\t [Job 138] Completed (_extract_mean_ts0).\n",
      "240725-08:57:32,994 nipype.workflow INFO:\n",
      "\t [Job 47] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:32,995 nipype.workflow INFO:\n",
      "\t [Job 48] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:32,996 nipype.workflow INFO:\n",
      "\t [Job 49] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:32,996 nipype.workflow INFO:\n",
      "\t [Job 50] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:32,997 nipype.workflow INFO:\n",
      "\t [Job 51] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:32,998 nipype.workflow INFO:\n",
      "\t [Job 80] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:33,0 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 17 tasks, and 19 jobs ready. Free memory (GB): 449.75/453.15, Free processors: 7/24.\n",
      "                     Currently running:\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "240725-08:57:33,63 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_523/datasink\".\n",
      "240725-08:57:33,68 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:33,65 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_544/datasink\".\n",
      "240725-08:57:33,67 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:33,67 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_544/datasink\".\n",
      "240725-08:57:33,69 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_548/datasink\".\n",
      "240725-08:57:33,69 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.001082s.\n",
      "240725-08:57:33,70 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:33,71 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_548/datasink\".\n",
      "240725-08:57:33,71 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_554/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:33,71 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_554/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:33,73 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:33,73 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:33,74 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:33,74 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.004864s.\n",
      "240725-08:57:33,74 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:33,86 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.013671s.\n",
      "240725-08:57:33,87 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.011681s.\n",
      "240725-08:57:33,164 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.089218s.\n",
      "240725-08:57:33,851 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 2.7152979999999998s.\n",
      "240725-08:57:34,692 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 3.556517s.\n",
      "240725-08:57:34,724 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 3.5874449999999998s.\n",
      "240725-08:57:34,792 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 3.64447s.\n",
      "240725-08:57:34,858 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 3.716239s.\n",
      "240725-08:57:34,862 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 3.718652s.\n",
      "240725-08:57:34,865 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 3.714576s.\n",
      "240725-08:57:34,965 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 3.8260709999999998s.\n",
      "240725-08:57:34,994 nipype.workflow INFO:\n",
      "\t [Job 139] Completed (_extract_mean_ts1).\n",
      "240725-08:57:34,995 nipype.workflow INFO:\n",
      "\t [Job 140] Completed (_extract_mean_ts0).\n",
      "240725-08:57:34,995 nipype.workflow INFO:\n",
      "\t [Job 141] Completed (_extract_mean_ts1).\n",
      "240725-08:57:34,996 nipype.workflow INFO:\n",
      "\t [Job 143] Completed (_extract_mean_ts1).\n",
      "240725-08:57:34,997 nipype.workflow INFO:\n",
      "\t [Job 147] Completed (_extract_mean_ts1).\n",
      "240725-08:57:34,997 nipype.workflow INFO:\n",
      "\t [Job 149] Completed (_extract_mean_ts1).\n",
      "240725-08:57:34,998 nipype.workflow INFO:\n",
      "\t [Job 154] Completed (_extract_mean_ts0).\n",
      "240725-08:57:34,998 nipype.workflow INFO:\n",
      "\t [Job 155] Completed (_extract_mean_ts1).\n",
      "240725-08:57:34,999 nipype.workflow INFO:\n",
      "\t [Job 81] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:35,0 nipype.workflow INFO:\n",
      "\t [Job 82] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:35,0 nipype.workflow INFO:\n",
      "\t [Job 83] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:34,999 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 3.853716s.\n",
      "240725-08:57:35,1 nipype.workflow INFO:\n",
      "\t [Job 84] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:35,2 nipype.workflow INFO:\n",
      "\t [Job 85] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:35,4 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 11 tasks, and 15 jobs ready. Free memory (GB): 450.95/453.15, Free processors: 13/24.\n",
      "                     Currently running:\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts0\n",
      "240725-08:57:35,11 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 3.864882s.\n",
      "240725-08:57:35,13 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 3.863106s.\n",
      "240725-08:57:35,16 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 3.875768s.\n",
      "240725-08:57:35,20 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 3.874742s.\n",
      "240725-08:57:35,26 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 3.884998s.\n",
      "240725-08:57:35,47 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 3.904375s.\n",
      "240725-08:57:35,87 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_555/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:35,87 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_549/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:35,87 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_549/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:35,89 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_555/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:35,89 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:35,89 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_554/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:35,89 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:35,90 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:35,90 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:35,90 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_555/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:35,91 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:35,91 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:35,91 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:35,91 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:35,92 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_549/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:35,92 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_549/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:35,93 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_554/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:35,93 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:35,94 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:35,95 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:35,94 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_555/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:35,95 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:35,94 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:35,96 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:35,96 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_558/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:35,97 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 3.956857s.\n",
      "240725-08:57:35,97 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:35,97 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:35,98 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 3.960409s.\n",
      "240725-08:57:35,99 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:35,97 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_558/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:35,100 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_558/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:35,102 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:35,103 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:35,103 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_558/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:35,104 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_559/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:35,105 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_559/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:35,105 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:35,107 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:35,108 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:35,804 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 2.728014s.\n",
      "240725-08:57:35,888 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 2.812904s.\n",
      "240725-08:57:36,996 nipype.workflow INFO:\n",
      "\t [Job 142] Completed (_extract_mean_ts0).\n",
      "240725-08:57:36,997 nipype.workflow INFO:\n",
      "\t [Job 144] Completed (_extract_mean_ts0).\n",
      "240725-08:57:36,998 nipype.workflow INFO:\n",
      "\t [Job 145] Completed (_extract_mean_ts1).\n",
      "240725-08:57:36,998 nipype.workflow INFO:\n",
      "\t [Job 146] Completed (_extract_mean_ts0).\n",
      "240725-08:57:36,999 nipype.workflow INFO:\n",
      "\t [Job 148] Completed (_extract_mean_ts0).\n",
      "240725-08:57:37,0 nipype.workflow INFO:\n",
      "\t [Job 150] Completed (_extract_mean_ts0).\n",
      "240725-08:57:37,0 nipype.workflow INFO:\n",
      "\t [Job 151] Completed (_extract_mean_ts1).\n",
      "240725-08:57:37,1 nipype.workflow INFO:\n",
      "\t [Job 152] Completed (_extract_mean_ts0).\n",
      "240725-08:57:37,1 nipype.workflow INFO:\n",
      "\t [Job 153] Completed (_extract_mean_ts1).\n",
      "240725-08:57:37,2 nipype.workflow INFO:\n",
      "\t [Job 156] Completed (_extract_mean_ts0).\n",
      "240725-08:57:37,2 nipype.workflow INFO:\n",
      "\t [Job 157] Completed (_extract_mean_ts1).\n",
      "240725-08:57:37,3 nipype.workflow INFO:\n",
      "\t [Job 52] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:37,3 nipype.workflow INFO:\n",
      "\t [Job 53] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:37,4 nipype.workflow INFO:\n",
      "\t [Job 60] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:37,5 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 10 tasks, and 12 jobs ready. Free memory (GB): 451.15/453.15, Free processors: 14/24.\n",
      "                     Currently running:\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "240725-08:57:37,71 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_551/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:37,76 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:37,73 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:37,73 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_551/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:37,73 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:37,75 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_551/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:37,75 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:37,75 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:37,76 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_552/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:37,75 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_552/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:37,76 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_553/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:37,77 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:37,76 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_551/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:37,77 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:37,77 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:37,78 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:37,78 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:37,78 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:37,78 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:37,79 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:37,79 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:37,79 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_552/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:37,80 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_553/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:37,80 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_552/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:37,81 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:37,81 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:37,81 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:37,81 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:37,82 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:37,82 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:37,82 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_553/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:37,84 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:37,84 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:37,85 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_553/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:37,86 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_549/datasink\".\n",
      "240725-08:57:37,86 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_554/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:37,87 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:37,87 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:37,87 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:37,88 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:37,89 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:37,89 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_554/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:37,90 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_549/datasink\".\n",
      "240725-08:57:37,91 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:37,91 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.001094s.\n",
      "240725-08:57:37,91 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:37,93 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:37,94 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_554/datasink\".\n",
      "240725-08:57:37,95 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.001027s.\n",
      "240725-08:57:37,95 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_559/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:37,95 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_559/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:37,96 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:37,98 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts1\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:37,98 nipype.workflow INFO:\n",
      "\t [Node] Executing \"_extract_mean_ts0\" <nipype.interfaces.fsl.utils.ImageMeants>\n",
      "240725-08:57:37,107 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.009016s.\n",
      "240725-08:57:38,919 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 3.8104240000000003s.\n",
      "240725-08:57:38,976 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 3.86698s.\n",
      "240725-08:57:39,0 nipype.workflow INFO:\n",
      "\t [Job 166] Completed (_extract_mean_ts0).\n",
      "240725-08:57:39,1 nipype.workflow INFO:\n",
      "\t [Job 167] Completed (_extract_mean_ts1).\n",
      "240725-08:57:39,2 nipype.workflow INFO:\n",
      "\t [Job 54] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:39,3 nipype.workflow INFO:\n",
      "\t [Job 55] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:39,4 nipype.workflow INFO:\n",
      "\t [Job 56] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:39,5 nipype.workflow INFO:\n",
      "\t [Job 57] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:39,6 nipype.workflow INFO:\n",
      "\t [Job 58] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:39,7 nipype.workflow INFO:\n",
      "\t [Job 59] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:39,8 nipype.workflow INFO:\n",
      "\t [Job 61] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:39,8 nipype.workflow INFO:\n",
      "\t [Job 86] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:39,9 nipype.workflow INFO:\n",
      "\t [Job 87] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:39,10 nipype.workflow INFO:\n",
      "\t [Job 94] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:39,12 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 10 tasks, and 8 jobs ready. Free memory (GB): 451.15/453.15, Free processors: 14/24.\n",
      "                     Currently running:\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "                       * _extract_mean_ts1\n",
      "                       * _extract_mean_ts0\n",
      "240725-08:57:39,83 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_551/datasink\".\n",
      "240725-08:57:39,88 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:39,84 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_551/datasink\".\n",
      "240725-08:57:39,84 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_552/datasink\".\n",
      "240725-08:57:39,85 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_552/datasink\".\n",
      "240725-08:57:39,86 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_553/datasink\".\n",
      "240725-08:57:39,86 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_559/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:39,87 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:39,87 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_553/datasink\".\n",
      "240725-08:57:39,88 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:39,88 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:39,88 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_554/datasink\".\n",
      "240725-08:57:39,89 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:39,89 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.001419s.\n",
      "240725-08:57:39,90 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_559/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:39,91 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.001902s.\n",
      "240725-08:57:39,91 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:39,92 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.00187s.\n",
      "240725-08:57:39,92 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:39,104 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:39,104 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:39,104 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:39,105 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:39,107 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.001447s.\n",
      "240725-08:57:39,135 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.028462s.\n",
      "240725-08:57:39,135 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.028739s.\n",
      "240725-08:57:39,135 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.028767s.\n",
      "240725-08:57:39,166 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 4.073516s.\n",
      "240725-08:57:39,173 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 4.074263s.\n",
      "240725-08:57:39,190 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 4.085672s.\n",
      "240725-08:57:39,201 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 4.100169s.\n",
      "240725-08:57:39,227 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 4.123598s.\n",
      "240725-08:57:39,228 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 4.121481s.\n",
      "240725-08:57:39,341 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 4.249861s.\n",
      "240725-08:57:39,403 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 4.307779s.\n",
      "240725-08:57:39,867 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts1\", elapsed time 2.767487s.\n",
      "240725-08:57:39,901 nipype.workflow INFO:\n",
      "\t [Node] Finished \"_extract_mean_ts0\", elapsed time 2.8016199999999998s.\n",
      "240725-08:57:41,3 nipype.workflow INFO:\n",
      "\t [Job 158] Completed (_extract_mean_ts0).\n",
      "240725-08:57:41,4 nipype.workflow INFO:\n",
      "\t [Job 159] Completed (_extract_mean_ts1).\n",
      "240725-08:57:41,5 nipype.workflow INFO:\n",
      "\t [Job 160] Completed (_extract_mean_ts0).\n",
      "240725-08:57:41,5 nipype.workflow INFO:\n",
      "\t [Job 161] Completed (_extract_mean_ts1).\n",
      "240725-08:57:41,6 nipype.workflow INFO:\n",
      "\t [Job 162] Completed (_extract_mean_ts0).\n",
      "240725-08:57:41,7 nipype.workflow INFO:\n",
      "\t [Job 163] Completed (_extract_mean_ts1).\n",
      "240725-08:57:41,7 nipype.workflow INFO:\n",
      "\t [Job 164] Completed (_extract_mean_ts0).\n",
      "240725-08:57:41,8 nipype.workflow INFO:\n",
      "\t [Job 165] Completed (_extract_mean_ts1).\n",
      "240725-08:57:41,8 nipype.workflow INFO:\n",
      "\t [Job 168] Completed (_extract_mean_ts0).\n",
      "240725-08:57:41,9 nipype.workflow INFO:\n",
      "\t [Job 169] Completed (_extract_mean_ts1).\n",
      "240725-08:57:41,9 nipype.workflow INFO:\n",
      "\t [Job 66] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:41,10 nipype.workflow INFO:\n",
      "\t [Job 88] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:41,11 nipype.workflow INFO:\n",
      "\t [Job 89] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:41,12 nipype.workflow INFO:\n",
      "\t [Job 90] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:41,13 nipype.workflow INFO:\n",
      "\t [Job 91] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:41,14 nipype.workflow INFO:\n",
      "\t [Job 92] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:41,14 nipype.workflow INFO:\n",
      "\t [Job 93] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:41,15 nipype.workflow INFO:\n",
      "\t [Job 95] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:41,16 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 0 tasks, and 6 jobs ready. Free memory (GB): 453.15/453.15, Free processors: 24/24.\n",
      "240725-08:57:41,87 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_555/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:41,96 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:41,89 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:41,89 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_555/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:41,90 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:41,91 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:41,91 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_558/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:41,91 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:41,91 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_555/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:41,92 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:41,93 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_555/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:41,93 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:41,93 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:41,94 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:41,94 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:41,94 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:41,95 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_558/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:41,95 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_558/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:41,96 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:41,97 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:41,97 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:41,98 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_558/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:41,98 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_559/datasink\".\n",
      "240725-08:57:41,99 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:41,100 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:41,100 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts0\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_559/extract_mean_ts/mapflow/_extract_mean_ts0\".\n",
      "240725-08:57:41,101 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:41,102 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts0\" - collecting precomputed outputs\n",
      "240725-08:57:41,102 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts0\" found cached.\n",
      "240725-08:57:41,104 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"_extract_mean_ts1\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_559/extract_mean_ts/mapflow/_extract_mean_ts1\".\n",
      "240725-08:57:41,105 nipype.workflow INFO:\n",
      "\t [Node] Cached \"_extract_mean_ts1\" - collecting precomputed outputs\n",
      "240725-08:57:41,105 nipype.workflow INFO:\n",
      "\t [Node] \"_extract_mean_ts1\" found cached.\n",
      "240725-08:57:41,124 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.021052s.\n",
      "240725-08:57:43,5 nipype.workflow INFO:\n",
      "\t [Job 62] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:43,7 nipype.workflow INFO:\n",
      "\t [Job 63] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:43,8 nipype.workflow INFO:\n",
      "\t [Job 64] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:43,9 nipype.workflow INFO:\n",
      "\t [Job 65] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:43,10 nipype.workflow INFO:\n",
      "\t [Job 67] Completed (extract_striatum_signal.extract_mean_ts).\n",
      "240725-08:57:43,10 nipype.workflow INFO:\n",
      "\t [Job 100] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:43,12 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 0 tasks, and 5 jobs ready. Free memory (GB): 453.15/453.15, Free processors: 24/24.\n",
      "240725-08:57:43,90 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_555/datasink\".\n",
      "240725-08:57:43,90 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_555/datasink\".\n",
      "240725-08:57:43,92 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_L_subject_id_558/datasink\".\n",
      "240725-08:57:43,93 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_558/datasink\".\n",
      "240725-08:57:43,93 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:43,93 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:43,94 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"extract_striatum_signal.datasink\" in \"/data/workflow_folders/extract_striatum_signal/_mask_STR_R_subject_id_559/datasink\".\n",
      "240725-08:57:43,95 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:43,96 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.00164s.\n",
      "240725-08:57:43,97 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:43,97 nipype.workflow INFO:\n",
      "\t [Node] Executing \"datasink\" <nipype.interfaces.io.DataSink>\n",
      "240725-08:57:43,97 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.001115s.\n",
      "240725-08:57:43,98 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.001537s.\n",
      "240725-08:57:43,119 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.021234s.\n",
      "240725-08:57:43,124 nipype.workflow INFO:\n",
      "\t [Node] Finished \"datasink\", elapsed time 0.02644s.\n",
      "240725-08:57:45,7 nipype.workflow INFO:\n",
      "\t [Job 96] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:45,8 nipype.workflow INFO:\n",
      "\t [Job 97] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:45,9 nipype.workflow INFO:\n",
      "\t [Job 98] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:45,9 nipype.workflow INFO:\n",
      "\t [Job 99] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:45,10 nipype.workflow INFO:\n",
      "\t [Job 101] Completed (extract_striatum_signal.datasink).\n",
      "240725-08:57:45,11 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 0 tasks, and 0 jobs ready. Free memory (GB): 453.15/453.15, Free processors: 24/24.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<networkx.classes.digraph.DiGraph at 0x7efc575f4490>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "workflow.run('MultiProc', plugin_args={'n_proc': 3})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5) Again, visualize what the output data structure looks like using `!tree`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[01;34m/data/extracted_signals\u001b[00m\n",
      "└── \u001b[01;34mextracted_timeseries\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_L_subject_id_197\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-197_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-197_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_L_subject_id_372\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-372_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-372_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_L_subject_id_381\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-381_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-381_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_L_subject_id_471\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-471_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-471_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_L_subject_id_483\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-483_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-483_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_L_subject_id_498\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-498_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-498_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_L_subject_id_523\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-523_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-523_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_L_subject_id_544\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-544_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-544_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_L_subject_id_548\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-548_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-548_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_L_subject_id_549\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-549_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-549_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_L_subject_id_551\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-551_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-551_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_L_subject_id_552\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-552_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-552_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_L_subject_id_553\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-553_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-553_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_L_subject_id_554\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-554_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-554_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_L_subject_id_555\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-555_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-555_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_L_subject_id_558\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-558_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-558_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_L_subject_id_559\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-559_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-559_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_R_subject_id_197\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-197_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-197_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_R_subject_id_372\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-372_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-372_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_R_subject_id_381\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-381_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-381_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_R_subject_id_471\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-471_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-471_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_R_subject_id_483\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-483_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-483_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_R_subject_id_498\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-498_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-498_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_R_subject_id_523\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-523_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-523_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_R_subject_id_544\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-544_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-544_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_R_subject_id_548\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-548_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-548_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_R_subject_id_549\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-549_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-549_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_R_subject_id_551\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-551_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-551_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_R_subject_id_552\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-552_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-552_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_R_subject_id_553\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-553_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-553_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_R_subject_id_554\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-554_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-554_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_R_subject_id_555\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-555_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-555_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    ├── \u001b[01;34m_mask_STR_R_subject_id_558\u001b[00m\n",
      "    │   ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "    │   │   └── \u001b[01;32msub-558_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    │   └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "    │       └── \u001b[01;32msub-558_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "    └── \u001b[01;34m_mask_STR_R_subject_id_559\u001b[00m\n",
      "        ├── \u001b[01;34m_extract_mean_ts0\u001b[00m\n",
      "        │   └── \u001b[01;32msub-559_task-SAT_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "        └── \u001b[01;34m_extract_mean_ts1\u001b[00m\n",
      "            └── \u001b[01;32msub-559_task-SAT_run-2_space-MNI152NLin2009cAsym_desc-preproc_bold_ts.txt\u001b[00m\n",
      "\n",
      "103 directories, 68 files\n"
     ]
    }
   ],
   "source": [
    "!tree /data/extracted_signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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