Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering analysis\n",
    "\n",
    "In this first notebook, we conduct a k-means clustering analysis on using included masks of each of the DMN regions.\n",
    "We use the simple neurosynth.analysis.cluster.magic function for clustering analysis. \n",
    "\n",
    "By default, the magic() function performs a k-means co-activation based analysis using the same methods as our manuscript. \n",
    "\n",
    "First lets import some basic necessities:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from nilearn import plotting as niplt\n",
    "from matplotlib.colors import ListedColormap\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, I load a previously generated Neurosynth dataset. This dataset was generated using version 0.6 of Neurosynth and the features are 60 topics generated using latent Dirichlet allocation (LDA). One can also generate a dataset using the latest version of Neurosynth and plug it into this analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from neurosynth.base.dataset import Dataset\n",
    "dataset = Dataset.load(\"data/neurosynth_60_0.6.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we use the magic function to perform the clustering analaysis. For each N we specify, an image is generated and placed in images/. Note that I specifiy that at least 80 studies must activate each voxel to be included in the analysis to ensure a robust classification. \n",
    "\n",
    "For each DMN region (MPFC,PCC,left-TPJ and right-TPJ), the following analysis will compute co-activation between each voxel within each region and the rest of the brain (reduced into 100 PCA components), and use the resulting distance matrix for classification. Note that this step may be computationally intensive. You may use the Clusterable class in neurosynth.analysis.cluster for a more custom analysis to avoid repeating the PCA for each classification if you desire.\n",
    "The number of clusters is chosen on the basis of sihouette score, consistent with the description in the manuscript."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#from neurosynth.analysis.cluster import magic\n",
    "#magic(dataset, roi_mask='masks/MPFC.nii.gz', min_studies_per_voxel=80, output_dir='images/MPFC/', n_clusters=2)\n",
    "#magic(dataset, roi_mask='masks/PCC.nii.gz', min_studies_per_voxel=80, output_dir='images/PCC/', n_clusters=3)\n",
    "#magic(dataset, roi_mask='masks/lTPJ.nii.gz', min_studies_per_voxel=80, output_dir='images/lTPJ/', n_clusters=3)\n",
    "#magic(dataset, roi_mask='masks/rTPJ.nii.gz', min_studies_per_voxel=80, output_dir='images/rTPJ/', n_clusters=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we use nilearn's plotting functions to display the results on saggital and coronal slices. Note that the figure shown on the manuscript was generated with MRICroGL with the same mask used here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<nilearn.plotting.displays.YSlicer at 0x117b29a50>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g2HZKl9STvb++Y8drpvvk90YP+bRp04ptd4tncsaYqvEgZ4ypmgEnV1mGr1tY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\nYA5/T9wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1152fcd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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irY5vBqXazJkzs845UtjzMMYU4cXDGFOEoy0ZMIFp5MiRya6Wi+ckgNFWWymq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\niP8P8HYMOlpRTWsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1173cddd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate color palette\n",
    "colors = [\"purple\",\"blue\",'red','green']\n",
    "colors = sns.xkcd_palette(colors)\n",
    "niplt.plot_roi('images/DMN/DMN.nii.gz', cut_coords=[4], display_mode='x', \n",
    "             draw_cross=False, cmap = ListedColormap(colors), alpha=0.8)\n",
    "colors = [\"red\",\"blue\",'red','blue']\n",
    "niplt.plot_roi('images/DMN/DMN.nii.gz', cut_coords=[-60], display_mode='y', \n",
    "             draw_cross=False, cmap = ListedColormap(colors), alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<nilearn.plotting.displays.XSlicer at 0x1174c01d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uKgg2taMK2ajrqKBbHleBtKkv3erPbgrlXVRtpnMofx577LFi/1Sgba986lOf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\nGk9yxpim8SRnjGma/w/YUEFc6uE8TwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d25f090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = colors = [\"amber\",\"dark blue grey\"]\n",
    "colors = sns.xkcd_palette(colors)\n",
    "niplt.plot_roi('images/MPFC/MPFCk2.nii.gz', cut_coords=[4], display_mode='x', \n",
    "             draw_cross=False, cmap = ListedColormap(colors), alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<nilearn.plotting.displays.XSlicer at 0x1177496d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IkRFR3ucZ0R4kzOP01pYKqfS8PqVGksOUQsq7SlSKG3qLGZxJT2YKmE3Bsj37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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117b37090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate color palette\n",
    "colors = [\"blue\",\"golden yellow\",\"red\"]\n",
    "colors = sns.xkcd_palette(colors)\n",
    "niplt.plot_roi('images/PCC/PCCk3.nii.gz', cut_coords=[4], display_mode='x', \n",
    "             draw_cross=False, cmap = ListedColormap(colors), alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<nilearn.plotting.displays.YSlicer at 0x1178d1d90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2tmwzQU1Fp1SCFlHSS8lCFsypubJKFvK75PO6/PLLW55nKGHPwxhThDcPY0wR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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117721610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Generate color palette\n",
    "colors = [\"green\",\"sunshine yellow\",\"red\"]\n",
    "colors = sns.xkcd_palette(colors)\n",
    "niplt.plot_roi('images/lTPJ/lTPJk3.nii.gz', cut_coords=[-66], display_mode='y', \n",
    "             draw_cross=False, cmap = ListedColormap(colors), alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<nilearn.plotting.displays.YSlicer at 0x117835bd0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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s5G/J53XJJZe0PM9Qwp6HMaYIbx7GmCIsWwaAH/3oR8mmhFEusKqfUAOX6GLT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117893a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate color palette\n",
    "colors = [\"indigo\",\"forest green\",\"orange\"]\n",
    "colors = sns.xkcd_palette(colors)\n",
    "niplt.plot_roi('images/rTPJ/rTPJk3.nii.gz', cut_coords=[-66], display_mode='y', \n",
    "             draw_cross=False, cmap = ListedColormap(colors), alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}