{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Connectome pipeline\n",
    "This tutorial shows how to use the NIAK connectome pipeline to generate seed-based functional connectivity maps on a single subject from the COBRE \"lightweight\" sample. \n",
    "  * **More documentation**: on the various pipeline options can be found on the [here](http://niak.simexp-lab.org/pipe_connectome.html). \n",
    "  * **.m script**: The notebook and associated Matlab script are available [here](https://nbviewer.jupyter.org/github/SIMEXP/niak_tutorials/blob/master/niak_tutorial_rmap_connectome.ipynb).\n",
    "  * **Time for completion**: this tutorial will take 5-10 minutes to complete. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing files\n",
    "\n",
    "### Functional MRI \n",
    "First download a small preprocessed fMRI dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "clear\n",
    "path_data = [pwd filesep];\n",
    "[status,msg,data_fmri] = niak_wget('cobre_lightweight20_nii');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When starting from fMRI data preprocessed with NIAK, it is possible to use `niak_grab_fmri_preprocess` on the output folder to collect the file names, as described in the [pipeline documentation](http://niak.simexp-lab.org/pipe_connectome.html). In this case, we explicitely list all the files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_pheno = [data_fmri.path filesep 'phenotypic_data.tsv.gz'];\n",
    "tab = niak_read_csv_cell(file_pheno);\n",
    "list_subject = tab(2:end,1);\n",
    "files_in = struct;\n",
    "for ss = 1:length(list_subject)\n",
    "    files_in.fmri.(list_subject{ss}).sess1.rest = [data_fmri.path filesep 'fmri_' list_subject{ss} '.nii.gz'];\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Brain parcellation\n",
    "The second input of the pipeline is a set of brain parcels. We will just download the so-called Cambridge functional template. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "[status,msg,data_template] = niak_wget('cambridge_template_mnc1');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to pick the parcellation into 7 distributed networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "files_in.network = [data_template.path filesep 'template_cambridge_basc_multiscale_sym_scale007.mnc.gz'];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List of seeds\n",
    "\n",
    "The next step is to generate a list of seeds. This requires creating a text file that looks like:\n",
    "```\n",
    "        , index\n",
    " MOTOR  , 3\n",
    " DMN    , 5\n",
    "```\n",
    "We are going to use NIAK's tool to write comma-separated values (CSV) in a file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "files_in.seeds = [path_data 'list_seeds.csv'];\n",
    "opt_csv.labels_x = { 'MOTOR' , 'DMN' }; % The labels for the network\n",
    "opt_csv.labels_y = { 'index' };\n",
    "tab = [3 ; 5];\n",
    "niak_write_csv(files_in.seeds,tab,opt_csv);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set the options of the pipeline\n",
    "Now we set up where to store the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "opt.folder_out = [path_data 'connectome'];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We set options such that we will not generate graph properties, just the correlation maps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "opt.flag_p2p = false; % No parcel-to-parcel correlation values\n",
    "opt.flag_global_prop = false; % No global graph properties\n",
    "opt.flag_local_prop  = false; % No local graph properties\n",
    "opt.flag_rmap = true; % Generate correlation maps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run the pipeline\n",
    "Now let's run the pipeline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding seed MOTOR (parcel number 3)\n",
      "Adding seed DMN (parcel number 5)\n",
      "\n",
      "Logs will be stored in /sandbox/home/Desktop/workshop_niak/connectome/logs/\n",
      "Generating dependencies ...\n",
      "   Percentage completed :  0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100- 0.16 sec\n",
      "Setting up the to-do list ...\n",
      "   I found 93 job(s) to do.\n",
      "I could not find any log file. This pipeline has not been started (yet?). Press CTRL-C to cancel.\n",
      "Deamon started on 16-May-2017 12:03:12\n",
      "16-May-2017 12:03:12 Starting the pipeline manager...\n",
      "16-May-2017 12:03:12 Starting the garbage collector...\n",
      "16-May-2017 12:03:12 Starting worker number 1...\n",
      "16-May-2017 12:03:12 Starting worker number 2...\n",
      "16-May-2017 12:03:12 Starting worker number 3...\n",
      "16-May-2017 12:03:12 Starting worker number 4...\n",
      "\n",
      "Pipeline started on 16-May-2017 12:03:14\n",
      "user: , host: 7bb4dbfb9281, system: unix\n",
      "****************************************\n",
      "16-May-2017 12:03:16 mask_rois            submitted  (1 run | 0 fail | 0 done | 92 left)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16-May-2017 12:03:37 rmap_seeds_40077     submitted  (7 run | 0 fail | 38 done | 48 left)\n",
      "16-May-2017 12:03:37 rmap_seeds_40045     submitted  (8 run | 0 fail | 38 done | 47 left)\n",
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      "16-May-2017 12:03:40 background           submitted  (8 run | 0 fail | 39 done | 46 left)\n",
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      "16-May-2017 12:03:40 network              submitted  (7 run | 0 fail | 41 done | 45 left)\n",
      "16-May-2017 12:03:40 img_rmap_MOTOR_40061 submitted  (8 run | 0 fail | 41 done | 44 left)\n",
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      "16-May-2017 12:03:43 rmap_seeds_40008     finished   (5 run | 0 fail | 46 done | 42 left)\n",
      "16-May-2017 12:03:43 average_rmap_MOTOR   submitted  (6 run | 0 fail | 46 done | 41 left)\n",
      "16-May-2017 12:03:43 average_rmap_DMN     submitted  (7 run | 0 fail | 46 done | 40 left)\n",
      "16-May-2017 12:03:43 img_rmap_MOTOR_40113 submitted  (8 run | 0 fail | 46 done | 39 left)\n",
      ".\n",
      "16-May-2017 12:04:15 network              finished   (7 run | 0 fail | 47 done | 39 left)\n",
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      ".\n",
      "16-May-2017 12:04:19 average_rmap_MOTOR   finished   (7 run | 0 fail | 50 done | 36 left)\n",
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      ".\n",
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      ".\n",
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      ".\n",
      "16-May-2017 12:06:02 img_rmap_MOTOR_40077 finished   (7 run | 0 fail | 69 done | 17 left)\n",
      "16-May-2017 12:06:02 img_rmap_DMN_40020   submitted  (8 run | 0 fail | 69 done | 16 left)\n",
      "16-May-2017 12:06:07 img_rmap_MOTOR_40045 finished   (7 run | 0 fail | 70 done | 16 left)\n",
      "16-May-2017 12:06:07 img_rmap_DMN_40065   submitted  (8 run | 0 fail | 70 done | 15 left)\n",
      "16-May-2017 13:01:12 No heartbeat for process psom1, counted as dead.\n",
      "16-May-2017 13:01:13 Restarting (1/4) worker number 1...\n",
      "16-May-2017 13:01:13 Worker 1 has been reset.\n",
      "16-May-2017 13:01:17 img_rmap_DMN_40117   submitted  (7 run | 0 fail | 70 done | 16 left)\n",
      "16-May-2017 13:01:17 img_rmap_DMN_40029   submitted  (8 run | 0 fail | 70 done | 15 left)\n",
      "16-May-2017 13:01:17 img_rmap_DMN_40117   finished   (7 run | 0 fail | 71 done | 15 left)\n",
      "16-May-2017 13:01:17 img_rmap_DMN_40122   submitted  (8 run | 0 fail | 71 done | 14 left)\n",
      "16-May-2017 13:01:17 img_rmap_DMN_40061   finished   (7 run | 0 fail | 72 done | 14 left)\n",
      "16-May-2017 13:01:17 img_rmap_DMN_40051   submitted  (8 run | 0 fail | 72 done | 13 left)\n",
      ".\n",
      "16-May-2017 13:01:45 img_rmap_DMN_40145   finished   (7 run | 0 fail | 73 done | 13 left)\n",
      "16-May-2017 13:01:45 img_rmap_DMN_40129   submitted  (8 run | 0 fail | 73 done | 12 left)\n",
      "16-May-2017 13:01:51 img_rmap_DMN_40113   finished   (7 run | 0 fail | 74 done | 12 left)\n",
      "16-May-2017 13:01:51 img_rmap_DMN_40037   submitted  (8 run | 0 fail | 74 done | 11 left)\n",
      ".\n",
      "16-May-2017 13:01:58 img_rmap_DMN_40029   finished   (7 run | 0 fail | 75 done | 11 left)\n",
      "16-May-2017 13:01:58 img_rmap_DMN_40133   finished   (6 run | 0 fail | 76 done | 11 left)\n",
      "16-May-2017 13:01:58 img_rmap_DMN_40093   submitted  (7 run | 0 fail | 76 done | 10 left)\n",
      "16-May-2017 13:01:58 img_rmap_DMN_40059   submitted  (8 run | 0 fail | 76 done | 9 left)\n",
      "16-May-2017 13:01:59 img_rmap_DMN_40122   finished   (7 run | 0 fail | 77 done | 9 left)\n",
      "16-May-2017 13:01:59 img_rmap_DMN_40104   submitted  (8 run | 0 fail | 77 done | 8 left)\n",
      "16-May-2017 13:02:18 img_rmap_DMN_40020   finished   (7 run | 0 fail | 78 done | 8 left)\n",
      "16-May-2017 13:02:19 img_rmap_DMN_40138   submitted  (8 run | 0 fail | 78 done | 7 left)\n",
      "16-May-2017 13:02:23 img_rmap_DMN_40065   finished   (7 run | 0 fail | 79 done | 7 left)\n",
      "16-May-2017 13:02:23 img_rmap_DMN_40003   submitted  (8 run | 0 fail | 79 done | 6 left)\n",
      ".\n",
      "16-May-2017 13:02:33 img_rmap_DMN_40051   finished   (7 run | 0 fail | 80 done | 6 left)\n",
      "16-May-2017 13:02:33 img_rmap_DMN_40008   submitted  (8 run | 0 fail | 80 done | 5 left)\n",
      "16-May-2017 13:02:36 img_rmap_DMN_40093   finished   (7 run | 0 fail | 81 done | 5 left)\n",
      "16-May-2017 13:02:36 img_rmap_DMN_40077   submitted  (8 run | 0 fail | 81 done | 4 left)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16-May-2017 13:02:53 img_rmap_DMN_40129   finished   (7 run | 0 fail | 82 done | 4 left)\n",
      "16-May-2017 13:02:53 img_rmap_DMN_40045   submitted  (8 run | 0 fail | 82 done | 3 left)\n",
      "16-May-2017 13:02:55 img_rmap_DMN_40037   finished   (7 run | 0 fail | 83 done | 3 left)\n",
      "16-May-2017 13:02:55 img_avg_MOTOR        submitted  (8 run | 0 fail | 83 done | 2 left)\n",
      ".\n",
      "16-May-2017 13:03:03 img_rmap_DMN_40104   finished   (7 run | 0 fail | 84 done | 2 left)\n",
      "16-May-2017 13:03:03 img_avg_DMN          submitted  (8 run | 0 fail | 84 done | 1 left)\n",
      "16-May-2017 13:03:06 img_rmap_DMN_40059   finished   (7 run | 0 fail | 85 done | 1 left)\n",
      "16-May-2017 13:03:08 img_rmap_DMN_40077   finished   (6 run | 0 fail | 86 done | 1 left)\n",
      "16-May-2017 13:03:25 img_rmap_DMN_40138   finished   (5 run | 0 fail | 87 done | 1 left)\n",
      "16-May-2017 13:03:25 img_rmap_DMN_40003   finished   (4 run | 0 fail | 88 done | 1 left)\n",
      "16-May-2017 13:03:29 img_avg_DMN          finished   (3 run | 0 fail | 89 done | 1 left)\n",
      "16-May-2017 13:03:29 Stopping idle worker 1 (not enough jobs left to do).\n",
      ".\n",
      "16-May-2017 13:03:34 img_rmap_DMN_40008   finished   (2 run | 0 fail | 90 done | 1 left)\n",
      "16-May-2017 13:03:34 Stopping idle worker 3 (not enough jobs left to do).\n",
      "16-May-2017 13:03:46 img_rmap_DMN_40045   finished   (1 run | 0 fail | 91 done | 1 left)\n",
      "16-May-2017 13:03:46 Stopping idle worker 2 (not enough jobs left to do).\n",
      "16-May-2017 13:03:47 img_avg_MOTOR        finished   (0 run | 0 fail | 92 done | 1 left)\n",
      "16-May-2017 13:03:47 rmap_report          submitted  (1 run | 0 fail | 92 done | 0 left)\n",
      "16-May-2017 13:03:48 rmap_report          finished   (0 run | 0 fail | 93 done | 0 left)\n",
      "16-May-2017 13:03:48 Stopping idle worker 4 (not enough jobs left to do).\n",
      "\n",
      "*******************************************\n",
      "Pipeline terminated on 16-May-2017 13:03:48\n",
      "All jobs have been successfully completed.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "opt.flag_test = false; \n",
    "[pipeline,opt] = niak_pipeline_connectome(files_in,opt); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reviewing outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pipeline generates an interatice dashboard to explore brain connectivity maps. Just open the file [connectome/report/index.html](connectome/report/index.html) in your browser."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the correlation maps have been generated in the subfolder called `rmap_seeds`, inside the output folder. There are first two average (across all subjects) maps, for each selected seed. We can have a quick look at them. Note that the files have been named using the identification codes in the file `files_in.seeds`.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ans =\n",
      "\n",
      "   27   32   26\n",
      "\n",
      "ans =\n",
      "\n",
      "   27   32   26\n",
      "\n"
     ]
    }
   ],
   "source": [
    "file_dmn   = [opt.folder_out filesep 'rmap_seeds' filesep 'average_rmap_DMN.nii.gz'];\n",
    "file_motor = [opt.folder_out filesep 'rmap_seeds' filesep 'average_rmap_MOTOR.nii.gz'];\n",
    "[hdr,rmap_dmn]   = niak_read_vol(file_dmn);\n",
    "[hdr,rmap_motor] = niak_read_vol(file_motor);\n",
    "size(rmap_dmn)\n",
    "size(rmap_motor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now use `niak_vol2img` to generate a series of axial slices for both maps. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LcUCKkyqgXC4vrU8NnUUNy0QQBEaj8couPQIOab64LcurTKj2Iev2g+2VDu2JUbfTNXZm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\nA5IkKRcMSJKkXPj/AWWuFItC41uRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "% The default-mode network\n",
    "opt_v = struct;\n",
    "opt_v.vol_limits = [0.25 0.7];\n",
    "opt_v.type_color = 'hot_cold';\n",
    "niak_montage(rmap_dmn,opt_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YE0XR9PT01NRUu93uVyKVxSqmR2ZI650Z0vgJgiB5UKlU+pVkzc3NrU3HpB7c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\nJEml8P8BqXJxvuftYEgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "% The sensorimotor network\n",
    "opt_v = struct;\n",
    "opt_v.vol_limits = [0.25 0.7];\n",
    "opt_v.type_color = 'hot_cold';\n",
    "niak_montage(rmap_motor,opt_v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are also two files which contain the seeds as binary masks. E.g.:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fIWZZtlwu/b6RlH/5X3+5w0L+V1lZPLqF9f/4y/OP/+/j07rRtm3/q3GkiuLiBNL0NE2z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\nAZAEgQRAEv4/KkiLOWmiWykAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "file_mask_dmn   = [opt.folder_out filesep 'rmap_seeds' filesep 'mask_DMN.nii.gz'];\n",
    "[hdr,mask_dmn]   = niak_read_vol(file_mask_dmn);\n",
    "opt_v = struct;\n",
    "opt_v.vol_limits = [0 1];\n",
    "opt_v.type_color = 'hot_cold';\n",
    "niak_montage(mask_dmn,opt_v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, there are also individual connectivity maps for each subject, named using the IDs we used in `files_in.fmri`. If multiple runs were specified per subject, the pipeline would average the maps from all runs.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TsaiH4FT4WscY+1lw6XiVZ+7UMfaTHWwN7IHz0pe7+5e8f7wbvahjmsnBM5ch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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "file_dmn_40003   = [opt.folder_out filesep 'rmap_seeds' filesep 'rmap_40003_DMN.nii.gz'];\n",
    "[hdr,rmap_dmn_40003]   = niak_read_vol(file_dmn_40003);\n",
    "opt_v = struct;\n",
    "opt_v.vol_limits = [0.4 1];\n",
    "opt_v.type_color = 'hot_cold';\n",
    "niak_montage(rmap_dmn_40003,opt_v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now load a connectome, aka an individual matrix of correlation between time series of different parcels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_connectome_40003   = [opt.folder_out filesep 'connectomes' filesep 'connectome_rois_40003.mat'];\n",
    "data = load(file_connectome_40003);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The connectome is stored in a variable called `conn`. The matrix has been vectorized, keeping only diagonal and lower triangular values. To get back a square matrix we need to run `niak_lvec2mat`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ans =\n",
      "\n",
      "   28    1\n",
      "\n",
      "ans =\n",
      "\n",
      "   7   7\n",
      "\n"
     ]
    }
   ],
   "source": [
    "size(data.conn)\n",
    "conn = niak_lvec2mat(data.conn);\n",
    "size(conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 7 parcels corresponding to our original brain parcellations. Parcels do not need to be numbered 1, 2, ... etc in the original parcellation volume. The numeric IDs of each parcel are stored in `data.ind_roi`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ans =\n",
      "\n",
      "   1\n",
      "   2\n",
      "   3\n",
      "   4\n",
      "   5\n",
      "   6\n",
      "   7\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data.ind_roi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So here parcels were actually numbered 1 to 7. We can visualize the connectivity matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ans =  1\n"
     ]
    },
    {
     "data": {
      "image/png": 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jD6SUUtd1fd8/6HzdJ1J65oIbAsp69O7L042NWwUb4VRGH0gfnkYA5V3LV+ogX6l4xh1I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\n/wM+isyHGMK0qAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "niak_visu_matrix(conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the connectivity values are Fisher transformation of correlation coefficients between average time series (for the inter-parcel connectivity), and Fisher transformation of average correlation between any pair of disctinct voxels inside the parcel (for intra-parcel connectivity). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally note that you can get a list of all files generated by the pipeline easily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "files =\n",
      "\n",
      "  scalar structure containing the fields:\n",
      "\n",
      "    network_rois = /sandbox/home/Desktop/workshop_niak/connectome/network_rois.nii.gz\n",
      "    connectome =\n",
      "\n",
      "      scalar structure containing the fields:\n",
      "\n",
      "        40003 = /sandbox/home/Desktop/workshop_niak/connectome/connectomes/connectome_rois_40003.mat\n",
      "        40008 = /sandbox/home/Desktop/workshop_niak/connectome/connectomes/connectome_rois_40008.mat\n",
      "        40020 = /sandbox/home/Desktop/workshop_niak/connectome/connectomes/connectome_rois_40020.mat\n",
      "        40029 = /sandbox/home/Desktop/workshop_niak/connectome/connectomes/connectome_rois_40029.mat\n",
      "        40037 = /sandbox/home/Desktop/workshop_niak/connectome/connectomes/connectome_rois_40037.mat\n",
      "        40045 = /sandbox/home/Desktop/workshop_niak/connectome/connectomes/connectome_rois_40045.mat\n",
      "        40051 = /sandbox/home/Desktop/workshop_niak/connectome/connectomes/connectome_rois_40051.mat\n",
      "        40059 = /sandbox/home/Desktop/workshop_niak/connectome/connectomes/connectome_rois_40059.mat\n",
      "        40061 = /sandbox/home/Desktop/workshop_niak/connectome/connectomes/connectome_rois_40061.mat\n",
      "        40065 = /sandbox/home/Desktop/workshop_niak/connectome/connectomes/connectome_rois_40065.mat\n",
      "        40077 = /sandbox/home/Desktop/workshop_niak/connectome/connectomes/connectome_rois_40077.mat\n",
      "        40093 = /sandbox/home/Desktop/workshop_niak/connectome/connectomes/connectome_rois_40093.mat\n",
      "        40104 = /sandbox/home/Desktop/workshop_niak/connectome/connectomes/connectome_rois_40104.mat\n",
      "        40113 = /sandbox/home/Desktop/workshop_niak/connectome/connectomes/connectome_rois_40113.mat\n",
      "        40117 = /sandbox/home/Desktop/workshop_niak/connectome/connectomes/connectome_rois_40117.mat\n",
      "        40122 = /sandbox/home/Desktop/workshop_niak/connectome/connectomes/connectome_rois_40122.mat\n",
      "        40129 = /sandbox/home/Desktop/workshop_niak/connectome/connectomes/connectome_rois_40129.mat\n",
      "        40133 = /sandbox/home/Desktop/workshop_niak/connectome/connectomes/connectome_rois_40133.mat\n",
      "        40138 = /sandbox/home/Desktop/workshop_niak/connectome/connectomes/connectome_rois_40138.mat\n",
      "        40145 = /sandbox/home/Desktop/workshop_niak/connectome/connectomes/connectome_rois_40145.mat\n",
      "\n",
      "    rmap =\n",
      "\n",
      "      scalar structure containing the fields:\n",
      "\n",
      "        DMN =\n",
      "\n",
      "          scalar structure containing the fields:\n",
      "\n",
      "            40003: 1x79 sq_string\n",
      "            40008: 1x79 sq_string\n",
      "            40020: 1x79 sq_string\n",
      "            40029: 1x79 sq_string\n",
      "            40037: 1x79 sq_string\n",
      "            40045: 1x79 sq_string\n",
      "            40051: 1x79 sq_string\n",
      "            40059: 1x79 sq_string\n",
      "            40061: 1x79 sq_string\n",
      "            40065: 1x79 sq_string\n",
      "            40077: 1x79 sq_string\n",
      "            40093: 1x79 sq_string\n",
      "            40104: 1x79 sq_string\n",
      "            40113: 1x79 sq_string\n",
      "            40117: 1x79 sq_string\n",
      "            40122: 1x79 sq_string\n",
      "            40129: 1x79 sq_string\n",
      "            40133: 1x79 sq_string\n",
      "            40138: 1x79 sq_string\n",
      "            40145: 1x79 sq_string\n",
      "\n",
      "        MOTOR =\n",
      "\n",
      "          scalar structure containing the fields:\n",
      "\n",
      "            40003: 1x81 sq_string\n",
      "            40008: 1x81 sq_string\n",
      "            40020: 1x81 sq_string\n",
      "            40029: 1x81 sq_string\n",
      "            40037: 1x81 sq_string\n",
      "            40045: 1x81 sq_string\n",
      "            40051: 1x81 sq_string\n",
      "            40059: 1x81 sq_string\n",
      "            40061: 1x81 sq_string\n",
      "            40065: 1x81 sq_string\n",
      "            40077: 1x81 sq_string\n",
      "            40093: 1x81 sq_string\n",
      "            40104: 1x81 sq_string\n",
      "            40113: 1x81 sq_string\n",
      "            40117: 1x81 sq_string\n",
      "            40122: 1x81 sq_string\n",
      "            40129: 1x81 sq_string\n",
      "            40133: 1x81 sq_string\n",
      "            40138: 1x81 sq_string\n",
      "            40145: 1x81 sq_string\n",
      "\n",
      "\n",
      "    mask =\n",
      "\n",
      "      scalar structure containing the fields:\n",
      "\n",
      "        DMN = /sandbox/home/Desktop/workshop_niak/connectome/rmap_seeds/mask_DMN.nii.gz\n",
      "        MOTOR = /sandbox/home/Desktop/workshop_niak/connectome/rmap_seeds/mask_MOTOR.nii.gz\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "files = niak_grab_connectome(opt.folder_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using that tool you can easily move to other toolboxes. Check FSL [PALM](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/PALM) for non-parametric linear regression tests on the connectivity maps (Matlab/Octave-based), and the [brain connectivity toolbox](https://sites.google.com/site/bctnet/Home) for graph analysis (Matlab/Octave-based)."
   ]
  }
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