Nilearn_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# An Introduction to interacting with nifti volumes in python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goals\n",
    "\n",
    "1. Demonstrate data input and output with nibabel.\n",
    "2. Demonstrate interacting with nifi headers.\n",
    "3. Demonstrate manipulating image volumes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nibabel and Nilearn are two very useful packages for neuroimaging data manipulation. \n",
    "\n",
    "[Nibabel](https://nipy.org/nibabel/) is a package designed for interacting with neuroimaging data in common formats.\n",
    "\n",
    "[Nilearn](https://nilearn.github.io/) is a much broader package that has the functionality to perform machine learning. There are examples of all the wonderful things you can do [here](https://nilearn.github.io/auto_examples/index.html). However, I will not be covering machine learning here. I will only be talking about some of the useful functions that nilearn has for plotting data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data input and output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's set the path to where our data are located. If you took part in the nipype tutorial, then this is the same data we used before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputpath='/Users/nicholashedger/Downloads/examp/bold.nii.gz'\n",
    "outputpath='/Users/nicholashedger/Desktop'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To help us interpret what comes below, I am going to plot the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<nilearn.plotting.displays.OrthoSlicer at 0x1c22a23cd0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 475.2x187.2 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import nilearn\n",
    "from nilearn import image\n",
    "from nilearn import plotting\n",
    "import matplotlib\n",
    "firstim=image.index_img(inputpath, 0)\n",
    "display=plotting.plot_anat(firstim)\n",
    "display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that this is a small FOV volume recorded from the posterior of the brain."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading nifti data into python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we import nibabel, and call the 'load' function to load our data into memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nibabel as nib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "img1=nib.load(inputpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<nibabel.nifti1.Nifti1Image at 0x1c404fd310>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that this is a nifti image. Nibabel actually seperates this file into 3 components, *the data*, *the header*, and *the affine*.\n",
    "\n",
    "Let's start with the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To access the data itself, we call the *get_data* function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=img1.get_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to learn something about the structure of the data. To do this, we can ask how many dimensions it has."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(97, 115, 97, 120)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the data has 4 dimensions. It is a functional image. The first 3 are space dimensions (X,Y,Z) and the final one is time. \n",
    "\n",
    "Thus our data are 97x115x97 voxels with 120 volumes.\n",
    "\n",
    "Our data are in a numpy array. This is just the standard format in which arrays are stored in python.\n",
    "\n",
    "Therefore, we can access the first volume of our data as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(97, 115, 97)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "firstvol=data[:,:,:,0]\n",
    "firstvol.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, the colons denote that we want 'all of' the x,y and z dimensions, but the 0 indicates that we only want the first volume. Note that, in python, indexing starts at 0.\n",
    "\n",
    "Therefore, if we ask for the 120th volume, as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 120 is out of bounds for axis 3 with size 120",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-3716d7eb7804>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlastvol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m120\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m: index 120 is out of bounds for axis 3 with size 120"
     ]
    }
   ],
   "source": [
    "lastvol=data[:,:,:,120]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will get an error. Since our indexing starts at 0, the 120th volume is at index 119."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lastvol=data[:,:,:,119]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can be a little confusing, since the way python does indexing is different to R, MATLAB etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's suppose that we wanted to access one slice of the data in the x dimension, we would do this as follows.\n",
    "\n",
    "Here for instance, we take slice 40 in the X dimension, from the first volume. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "myslice=data[40,:,:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since a slice is 2 dimensional, we can view it as as we would any other image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c22c15b90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(myslice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Often, we may be quite interested in plotting data over time.\n",
    "\n",
    "Let's say for instance, we wanted to plot data from an individual voxel over the course of the run.\n",
    "\n",
    "Here, we would populate the first 3 dimensions with an index to indicate the voxel we want, but add a colon in the time dimension to indicate we want the whole timeseries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts=data[58,35,24,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c21bd4910>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Arithmetic on volumes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Often, we may want to perform some arithmetic on functional volumes. For instance, sometimes it is useful to make a mean functional image.\n",
    "\n",
    "Since our data are in numpy format, we can use numpy functions to manipulate them. Therefore, we can simply ask for the mean, specifying that we want to average over the time dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(97, 115, 97)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "meanvol=np.mean(data,axis=3) # Time is the 3rd dimension\n",
    "meanvol.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then save this mean functional volume to a nifti image as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "new_img = nib.Nifti1Image(meanvol,affine=img1.affine,header=img1.header)\n",
    "fname=os.path.join(outputpath,'meanvol.nii.gz')\n",
    "nib.save(new_img,fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that, in order to save to a nifti file, we also need to specify 'header' and 'affine' information (More on this in a moment).\n",
    "\n",
    "Now we plot our mean functional image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 475.2x187.2 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display=plotting.plot_anat(fname)\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to caculating a mean over time, we may also want to calculate a mean across 2 functional runs. To do this, we would do as follows.\n",
    "\n",
    "Note that we only have 1 set of data and so here we are just averaging across two of the same run (ordinarily we would average across two different runs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "runs = [None]*2 # Make an empty list \n",
    "runs[0]=data # Data from the first run\n",
    "runs[1]=data # Data from the second run\n",
    "runs=np.array(runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 97, 115, 97, 120)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runs.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a new numpy array, with the run number as a new first axis. Therefore, we just have to average across this new axis to average the data across runs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_average=np.mean(runs,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(97, 115, 97, 120)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_average.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The nifti header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to access the nifti header, we do as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'nibabel.nifti1.Nifti1Header'> object, endian='<'\n",
      "sizeof_hdr      : 348\n",
      "data_type       : \n",
      "db_name         : \n",
      "extents         : 0\n",
      "session_error   : 0\n",
      "regular         : r\n",
      "dim_info        : 0\n",
      "dim             : [  4  97 115  97 120   1   1   1]\n",
      "intent_p1       : 0.0\n",
      "intent_p2       : 0.0\n",
      "intent_p3       : 0.0\n",
      "intent_code     : none\n",
      "datatype        : float32\n",
      "bitpix          : 32\n",
      "slice_start     : 0\n",
      "pixdim          : [1.  2.  2.  2.  1.5 1.  1.  1. ]\n",
      "vox_offset      : 0.0\n",
      "scl_slope       : nan\n",
      "scl_inter       : nan\n",
      "slice_end       : 0\n",
      "slice_code      : unknown\n",
      "xyzt_units      : 10\n",
      "cal_max         : 251009.53\n",
      "cal_min         : -21153.723\n",
      "slice_duration  : 0.0\n",
      "toffset         : 0.0\n",
      "glmax           : 0\n",
      "glmin           : 0\n",
      "descrip         : xform matrices modified by FixHeaderApplyTransforms (niworkflows v1.1.12).\n",
      "aux_file        : \n",
      "qform_code      : mni\n",
      "sform_code      : mni\n",
      "quatern_b       : 0.0\n",
      "quatern_c       : 0.0\n",
      "quatern_d       : 0.0\n",
      "qoffset_x       : -96.5\n",
      "qoffset_y       : -132.5\n",
      "qoffset_z       : -78.5\n",
      "srow_x          : [  2.    0.    0.  -96.5]\n",
      "srow_y          : [   0.     2.     0.  -132.5]\n",
      "srow_z          : [  0.    0.    2.  -78.5]\n",
      "intent_name     : \n",
      "magic           : n+1\n"
     ]
    }
   ],
   "source": [
    "print(img1.header)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see a whole bunch of fields. Here I define a function for getting the most important stuff."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getniftibits(file):\n",
    "    import pandas as pd\n",
    "    \n",
    "    nifti = nib.load(file)\n",
    "    VOXSIZE = nifti.header['pixdim'][1:4]\n",
    "    SHAPE= (nifti.header['dim'][1:5])\n",
    "    TR = (nifti.header['pixdim'][4:5])\n",
    "    VOXFRAME=pd.DataFrame(VOXSIZE)\n",
    "    VOXFRAME=VOXFRAME.T\n",
    "    SHAPEFRAME=pd.DataFrame(SHAPE)\n",
    "    SHAPEFRAME=SHAPEFRAME.T\n",
    "    VOXFRAME.columns=['VoxsizeX','VoxsizeY','VoxsizeZ']\n",
    "    SHAPEFRAME.columns=['ShapeX','ShapeY','ShapeZ','Volumes']\n",
    "    CFRAMEi=pd.concat([VOXFRAME,SHAPEFRAME],axis=1)\n",
    "    CFRAMEi['TR'] = TR \n",
    "    return(CFRAMEi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VoxsizeX</th>\n",
       "      <th>VoxsizeY</th>\n",
       "      <th>VoxsizeZ</th>\n",
       "      <th>ShapeX</th>\n",
       "      <th>ShapeY</th>\n",
       "      <th>ShapeZ</th>\n",
       "      <th>Volumes</th>\n",
       "      <th>TR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>97</td>\n",
       "      <td>115</td>\n",
       "      <td>97</td>\n",
       "      <td>120</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VoxsizeX  VoxsizeY  VoxsizeZ  ShapeX  ShapeY  ShapeZ  Volumes   TR\n",
       "0       2.0       2.0       2.0      97     115      97      120  1.5"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v=getniftibits(inputpath)\n",
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the voxel size (mm), dimensions, repetition time (s) and number of volumes are all stored in here. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *srow* data describe affine transformations that explain how our voxel data relate to positions relative to the magnet isocentre. You can read more on this [here](https://nipy.org/nibabel/coordinate_systems.html) and [here](https://nilearn.github.io/auto_examples/04_manipulating_images/plot_affine_transformation.html#sphx-glr-auto-examples-04-manipulating-images-plot-affine-transformation-py)\n",
    "\n",
    "This affine information can also be accessed more directly as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   2. ,    0. ,    0. ,  -96.5],\n",
       "       [   0. ,    2. ,    0. , -132.5],\n",
       "       [   0. ,    0. ,    2. ,  -78.5],\n",
       "       [   0. ,    0. ,    0. ,    1. ]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img1.affine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resampling images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's suppose that we wanted to resample some data.\n",
    "\n",
    "A possible reason for this is that we have some data sampled in MNI 2mm space and we want to know what data correspond to regions of interest in an atlas.\n",
    "\n",
    "Unfortunately, our atlas is in MNI 1mm space. Therefore, it may make sense to downsample the atlas into the same space as our data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nilearn.datasets import load_mni152_template\n",
    "template = load_mni152_template()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91, 109, 91)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "template.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_img = image.resample_to_img(img1, template)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fname=os.path.join(outputpath,'resampled.nii.gz')\n",
    "nib.save(new_img,fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img1.affine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_img.affine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cimg=nilearn.image.crop_img(img1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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