content/Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing\n",
    "*Written by Luke Chang*\n",
    "\n",
    "Being able to study brain activity associated with cognitive processes in humans is an amazing achievement. However, as we have noted throughout this course, there is an extraordinary amount of noise and a very low levels of signal, which makes it difficult to make inferences about the function of the brain using this BOLD imaging. A critical step before we can perform any analyses is to do our best to remove as much of the noise as possible. The series of steps to remove noise comprise our *neuroimaging data **preprocessing** pipeline*. See slides on our preprocessing lecture {download}`here <../images/lectures/Preprocessing.pdf>`.\n",
    "\n",
    "![preprocessing](../images/preprocessing/preprocessing.png)\n",
    "\n",
    "In this lab, we will go over the basics of preprocessing fMRI data using the [fmriprep](https://fmriprep.readthedocs.io/en/stable/) preprocessing pipeline. We will cover:\n",
    "\n",
    " - Image transformations\n",
    " - Head motion correction\n",
    " - Spatial Normalization\n",
    " - Spatial Smoothing\n",
    " \n",
    "There are other preprocessing steps that are also common, but not necessarily performed by all labs such as slice timing and distortion correction. We will not be discussing these in depth outside of the videos.\n",
    "\n",
    "Let's start with watching a short video by Martin Lindquist to get a general overview of the main steps of preprocessing and the basics of how to transform images and register them to other images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-18T21:34:22.934875Z",
     "start_time": "2020-04-18T21:34:22.797618Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
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      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7ff7f13dbdd0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "\n",
    "YouTubeVideo('Qc3rRaJWOc4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image Transformations \n",
    "\n",
    "Ok, now let's dive deeper into how we can transform images into different spaces using linear transformations.\n",
    "\n",
    "Recall from our introduction to neuroimaging data lab, that neuroimaging data is typically stored in a nifti container, which contains a 3D or 4D matrix of the voxel intensities and also an affine matrix, which provides instructions for how to transform the matrix into another space.\n",
    "\n",
    "Let's create an interactive plot using ipywidgets so that we can get an intuition for how these affine matrices can be used to transform a 3D image.\n",
    "\n",
    "We can move the sliders to play with applying rigid body  transforms to a 3D cube. A rigid body transformation has 6 parameters: translation in x,y, & z, and rotation around each of these axes. The key thing to remember is that a rigid body transform doesn't allow the image to be fundamentally changed. A full 12 parameter affine transformation adds an additional 3 parameters each for scaling and shearing, which can change the shape of the cube.\n",
    "\n",
    "Try moving some of the sliders around. Note that the viewer is a little slow. Each time you move a slider it is applying an affine transformation to the matrix and re-plotting. \n",
    "\n",
    "Translation moves the cube in x, y, and z dimensions.\n",
    "\n",
    "We can also rotate the cube around the x, y, and z axes where the origin is the center point. Continuing to rotate around the point will definitely lead to the cube leaving the current field of view, but it will come back if you keep rotating it.\n",
    "\n",
    "You'll notice that every time we change the slider and apply a new affine transformation that the cube gets a little distorted with aliasing. Often we need to interpolate the image after applying a transformation to fill in the gaps after applying a transformation. It is important to keep in mind that every time we apply an affine transformation to our images, it is actually not a perfect representation of the original data. Additional steps like reslicing, interpolation, and spatial smoothing can help with this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 612,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T04:02:27.817988Z",
     "start_time": "2020-04-17T04:02:26.420706Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "31816ba76de64c04bd7cc2752a50c81e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.0, description='trans_x', max=10.0, min=-10.0, step=1.0), FloatSlide…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_rigid_body_affine_transformation(trans_x=0, trans_y=0, trans_z=0, rot_x=0, rot_y=0, rot_z=0)>"
      ]
     },
     "execution_count": 612,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from mpl_toolkits import mplot3d\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from nibabel.affines import apply_affine, from_matvec, to_matvec\n",
    "from scipy.ndimage import affine_transform, map_coordinates\n",
    "import nibabel as nib\n",
    "from ipywidgets import interact, FloatSlider\n",
    "\n",
    "def plot_rigid_body_transformation(trans_x=0, trans_y=0, trans_z=0, rot_x=0, rot_y=0, rot_z=0):\n",
    "    '''This plot creates an interactive demo to illustrate the parameters of a rigid body transformation'''\n",
    "    fov = 30\n",
    "    radius = 10\n",
    "    x, y, z = np.indices((fov, fov, fov))\n",
    "    cube = ((x > fov//2 - radius//2) & (x < fov//2 + radius//2)) & ((y > fov//2 - radius//2) & (y < fov//2 + radius//2)) & ((z > fov//2 - radius//2) & (z < fov//2 + radius//2 ))\n",
    "    cube = cube.astype(int)\n",
    "\n",
    "    vec = np.array([trans_x, trans_y, trans_z])\n",
    "    \n",
    "    rot_x = np.radians(rot_x)\n",
    "    rot_y = np.radians(rot_y)\n",
    "    rot_z = np.radians(rot_z)\n",
    "    rot_axis1 = np.array([[1, 0, 0],\n",
    "                         [0, np.cos(rot_x), -np.sin(rot_x)],\n",
    "                         [0, np.sin(rot_x), np.cos(rot_x)]])\n",
    "\n",
    "    rot_axis2 = np.array([[np.cos(rot_y), 0, np.sin(rot_y)],\n",
    "                         [0, 1, 0],\n",
    "                         [-np.sin(rot_y), 0, np.cos(rot_y)]])\n",
    "\n",
    "    rot_axis3 = np.array([[np.cos(rot_z), -np.sin(rot_z), 0],\n",
    "                         [np.sin(rot_z), np.cos(rot_z), 0],\n",
    "                         [0, 0, 1]])\n",
    "\n",
    "    rotation = rot_axis1 @ rot_axis2 @ rot_axis3\n",
    "    \n",
    "    affine = from_matvec(rotation, vec)\n",
    "    \n",
    "    i_coords, j_coords, k_coords = np.meshgrid(range(cube.shape[0]), range(cube.shape[1]), range(cube.shape[2]), indexing='ij')\n",
    "    coordinate_grid = np.array([i_coords, j_coords, k_coords])\n",
    "    coords_last = coordinate_grid.transpose(1, 2, 3, 0)\n",
    "    transformed = apply_affine(affine, coords_last)\n",
    "    coords_first = transformed.transpose(3, 0, 1, 2)\n",
    "\n",
    "    fig = plt.figure(figsize=(15, 12))\n",
    "    ax = plt.axes(projection='3d')\n",
    "    ax.voxels(map_coordinates(cube, coords_first))\n",
    "    ax.set_xlabel('x', fontsize=16)\n",
    "    ax.set_ylabel('y', fontsize=16)\n",
    "    ax.set_zlabel('z', fontsize=16)\n",
    "\n",
    "interact(plot_rigid_body_transformation, \n",
    "         trans_x=FloatSlider(value=0, min=-10, max=10, step=1),\n",
    "         trans_y=FloatSlider(value=0, min=-10, max=10, step=1),\n",
    "         trans_z=FloatSlider(value=0, min=-10, max=10, step=1),\n",
    "         rot_x=FloatSlider(value=0, min=0, max=360, step=15),\n",
    "         rot_y=FloatSlider(value=0, min=0, max=360, step=15),\n",
    "         rot_z=FloatSlider(value=0, min=0, max=360, step=15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-18T21:35:14.805478Z",
     "start_time": "2020-04-18T21:35:14.603928Z"
    }
   },
   "source": [
    "![rigidbody](../images/preprocessing/Rigid_Body.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, so what's going on behind the sliders?\n",
    "\n",
    "Let's borrow some of the material available in the nibabel [documentation](https://nipy.org/nibabel/coordinate_systems.html) to understand how these transformations work.\n",
    "\n",
    "The affine matrix is a way to transform images between spaces. In general, we have some voxel space coordinate $(i, j, k)$, and we want to figure out how to remap this into a reference space coordinate $(x, y, z)$.\n",
    "\n",
    "It can be useful to think of this as a coordinate transform function $f$ that accepts a voxel coordinate in the original space as an *input* and returns a coordinate in the *output* reference space:\n",
    "\n",
    "$$(x, y, z) = f(i, j, k)$$\n",
    "\n",
    "In theory $f$ could be a complicated non-linear function, but in practice we typically assume that the relationship between $(i, j, k)$ and $(x, y, z)$ is linear (or *affine*), and can be encoded with linear affine transformations comprising translations, rotations, and zooms.\n",
    "\n",
    "Scaling (zooming) in three dimensions can be represented by a diagonal 3 by 3\n",
    "matrix.  Here's how to zoom the first dimension by $p$, the second by $q$ and\n",
    "the third by $r$ units:\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "x\\\\\n",
    "y\\\\\n",
    "z\n",
    "\\end{bmatrix} \n",
    "\\quad\n",
    "=\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "pi\\\\\n",
    "qj\\\\\n",
    "rk\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "=\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "p & 0 & 0 \\\\\n",
    "0 & q & 0 \\\\\n",
    "0 & 0 & r\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "i\\\\\n",
    "j\\\\\n",
    "k\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "A rotation in three dimensions can be represented as a 3 by 3 *rotation matrix* [wikipedia rotation matrix](https://en.wikipedia.org/wiki/Rotation_matrix). For example, here is a rotation by $\\theta$ radians around the third array axis:\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "x \\\\\n",
    "y \\\\\n",
    "z\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "=\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "\\cos(\\theta) &  -\\sin(\\theta) & 0 \\\\\n",
    "\\sin(\\theta) & \\cos(\\theta) & 0 \\\\\n",
    "0 & 0 & 1 \\\\\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "i \\\\\n",
    "j \\\\\n",
    "k\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "This is a rotation by $\\phi$ radians around the second array axis:\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "x \\\\\n",
    "y \\\\\n",
    "z \\\\\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "=\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "\\cos(\\phi) & 0 & \\sin(\\phi) \\\\\n",
    "0 & 1 & 0 \\\\\n",
    "-\\sin(\\phi) & 0 & \\cos(\\phi) \\\\\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "i \\\\\n",
    "j \\\\\n",
    "k \n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "A rotation of $\\gamma$ radians around the first array axis:\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "x\\\\\n",
    "y\\\\\n",
    "z\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "=\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "1 & 0 & 0 \\\\\n",
    "0 & \\cos(\\gamma) & -\\sin(\\gamma) \\\\\n",
    "0 & \\sin(\\gamma) & \\cos(\\gamma) \\\\\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "i \\\\\n",
    "j \\\\\n",
    "k\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "Zoom and rotation matrices can be combined by matrix multiplication.\n",
    "\n",
    "Here's a scaling of $p, q, r$ units followed by a rotation of $\\theta$ radians\n",
    "around the third axis followed by a rotation of $\\phi$ radians around the\n",
    "second axis:\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "x \\\\\n",
    "y \\\\\n",
    "z\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "=\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "\\cos(\\phi) & 0 & \\sin(\\phi) \\\\\n",
    "0 & 1 & 0 \\\\\n",
    "-\\sin(\\phi) & 0 & \\cos(\\phi) \\\\\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "\\cos(\\theta) &  -\\sin(\\theta) & 0 \\\\\n",
    "\\sin(\\theta) & \\cos(\\theta) & 0 \\\\\n",
    "0 & 0 & 1 \\\\\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "p & 0 & 0 \\\\\n",
    "0 & q & 0 \\\\\n",
    "0 & 0 & r \\\\\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "i\\\\\n",
    "j\\\\\n",
    "k\\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "This can also be written:\n",
    "\n",
    "\n",
    "$$\n",
    "M\n",
    "\\quad\n",
    "=\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "\\cos(\\phi) & 0 & \\sin(\\phi) \\\\\n",
    "0 & 1 & 0 \\\\\n",
    "-\\sin(\\phi) & 0 & \\cos(\\phi) \\\\\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "\\cos(\\theta) &  -\\sin(\\theta) & 0 \\\\\n",
    "\\sin(\\theta) & \\cos(\\theta) & 0 \\\\\n",
    "0 & 0 & 1 \\\\\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "p & 0 & 0 \\\\\n",
    "0 & q & 0 \\\\\n",
    "0 & 0 & r \\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "x \\\\\n",
    "y \\\\\n",
    "z \n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "=\n",
    "\\quad\n",
    "M\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "i \\\\\n",
    "j \\\\\n",
    "k\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "This might be obvious because the matrix multiplication is the result of\n",
    "applying each transformation in turn on the coordinates output from the\n",
    "previous transformation. Combining the transformations into a single matrix\n",
    "$M$ works because matrix multiplication is associative -- $ABCD = (ABC)D$.\n",
    "\n",
    "A translation in three dimensions can be represented as a length 3 vector to\n",
    "be added to the length 3 coordinate.  For example, a translation of $a$ units\n",
    "on the first axis, $b$ on the second and $c$ on the third might be written\n",
    "as:\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "x \\\\\n",
    "y \\\\\n",
    "z\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "=\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "i \\\\\n",
    "j \\\\\n",
    "k\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "+\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "a \\\\\n",
    "b \\\\\n",
    "c \n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "We can write our function $f$ as a combination of matrix multiplication by some 3 by 3 rotation / zoom matrix $M$ followed by addition of a 3 by 1 translation vector $(a, b, c)$\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "x \\\\\n",
    "y \\\\\n",
    "z\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "=\n",
    "\\quad\n",
    "M\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "i \\\\\n",
    "j \\\\\n",
    "k\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "+\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "a \\\\\n",
    "b \\\\\n",
    "c\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "We could record the parameters necessary for $f$ as the 3 by 3 matrix, $M$\n",
    "and the 3 by 1 vector $(a, b, c)$.\n",
    "\n",
    "In fact, the 4 by 4 image *affine array* includes this exact information. If $m_{i,j}$ is the value in row $i$ column $j$ of matrix $M$, then the image affine matrix $A$ is:\n",
    "\n",
    "$$\n",
    "A\n",
    "\\quad\n",
    "=\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "m_{1,1} & m_{1,2} & m_{1,3} & a \\\\\n",
    "m_{2,1} & m_{2,2} & m_{2,3} & b \\\\\n",
    "m_{3,1} & m_{3,2} & m_{3,3} & c \\\\\n",
    "0 & 0 & 0 & 1 \\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "Why the extra row of $[0, 0, 0, 1]$?  We need this row because we have rephrased the combination of rotations / zooms and translations as a transformation in *homogenous coordinates* (see [wikipedia homogenous\n",
    "coordinates](https://en.wikipedia.org/wiki/Homogeneous_coordinates)). This is a trick that allows us to put the translation part into the same matrix as the rotations / zooms, so that both translations and rotations / zooms can be applied by matrix multiplication. In order to make this work, we have to add an extra 1 to our input and output coordinate vectors:\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "x \\\\\n",
    "y \\\\\n",
    "z \\\\\n",
    "1\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "=\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "m_{1,1} & m_{1,2} & m_{1,3} & a \\\\\n",
    "m_{2,1} & m_{2,2} & m_{2,3} & b \\\\\n",
    "m_{3,1} & m_{3,2} & m_{3,3} & c \\\\\n",
    "0 & 0 & 0 & 1 \\\\\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "i \\\\\n",
    "j \\\\\n",
    "k \\\\\n",
    "1\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "This results in the same transformation as applying $M$ and $(a, b, c)$ separately. One advantage of encoding transformations this way is that we can combine two sets of rotations, zooms, translations by matrix multiplication of the two corresponding affine matrices.\n",
    "\n",
    "In practice, although it is common to combine 3D transformations using 4 x 4 affine matrices, we usually *apply* the transformations by breaking up the affine matrix into its component $M$ matrix and $(a, b, c)$ vector and doing:\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "x \\\\\n",
    "y \\\\\n",
    "z\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "=\n",
    "\\quad\n",
    "M\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "i \\\\\n",
    "j \\\\\n",
    "k\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "+\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "a \\\\\n",
    "b \\\\\n",
    "c\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "As long as the last row of the 4 by 4 is $[0, 0, 0, 1]$, applying the transformations in this way is mathematically the same as using the full 4 by 4 form, without the inconvenience of adding the extra 1 to our input and output vectors.\n",
    "\n",
    "You can think of the image affine as a combination of a series of transformations to go from voxel coordinates to mm coordinates in terms of the magnet isocenter. Here is the EPI affine broken down into a series of transformations, with the results shown on the localizer image:\n",
    "\n",
    "<img src=\"https://nipy.org/nibabel/_images/illustrating_affine.png\" />\n",
    "\n",
    "Applying different affine transformations allows us to rotate, reflect, scale, and shear the image."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cost Functions\n",
    "Now that we have learned how affine transformations can be applied to transform images into different spaces, how can we use this to register one brain image to another image?\n",
    "\n",
    "The key is to identify a way to quantify how aligned the two images are to each other. Our visual systems are very good at identifying when two images are aligned, however, we need to create an alignment measure. These measures are often called *cost functions*. \n",
    "\n",
    "There are many different types of cost functions depending on the types of images that are being aligned. For example, a common cost function is called minimizing the sum of the squared differences and is similar to how regression lines are fit to minimize deviations from the observed data. This measure works best if the images are of the same type and have roughly equivalent signal intensities.\n",
    "\n",
    "Let's create another interactive plot and find the optimal X & Y translation parameters that minimize the difference between a two-dimensional target image to a reference image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 619,
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    "ExecuteTime": {
     "end_time": "2020-04-17T05:31:59.845770Z",
     "start_time": "2020-04-17T05:31:59.350358Z"
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   "outputs": [
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       "interactive(children=(FloatSlider(value=0.0, description='trans_x', max=0.0, min=-30.0, step=1.0), FloatSlider…"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_affine_cost(trans_x=0, trans_y=0)>"
      ]
     },
     "execution_count": 619,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_affine_cost(trans_x=0, trans_y=0):\n",
    "    '''This function creates an interactive demo to highlight how a cost function works in image registration.'''\n",
    "    fov = 30\n",
    "    radius = 15\n",
    "    x, y = np.indices((fov, fov))\n",
    "    square1 = (x < radius-2) & (y < radius-2)\n",
    "    square2 = ((x > fov//2 - radius//2) & (x < fov//2 + radius//2)) & ((y > fov//2 - radius//2) & (y < fov//2 + radius//2))\n",
    "    square1 = square1.astype(float)\n",
    "    square2 = square2.astype(float)\n",
    "\n",
    "    vec = np.array([trans_y, trans_x])\n",
    "    \n",
    "    affine = from_matvec(np.eye(2), vec)\n",
    "    \n",
    "    i_coords, j_coords = np.meshgrid(range(square1.shape[0]), range(square1.shape[1]), indexing='ij')\n",
    "    coordinate_grid = np.array([i_coords, j_coords])\n",
    "    coords_last = coordinate_grid.transpose(1, 2, 0)\n",
    "    transformed = apply_affine(affine, coords_last)\n",
    "    coords_first = transformed.transpose(2, 0, 1)\n",
    "    \n",
    "    transformed_square = map_coordinates(square1, coords_first)\n",
    "    f,a = plt.subplots(ncols=3, figsize=(15, 5))\n",
    "    a[0].imshow(transformed_square)\n",
    "    a[0].set_xlabel('x', fontsize=16)\n",
    "    a[0].set_ylabel('y', fontsize=16)\n",
    "    a[0].set_title('Target Image', fontsize=18)\n",
    "    \n",
    "    a[1].imshow(square2)\n",
    "    a[1].set_xlabel('x', fontsize=16)\n",
    "    a[1].set_ylabel('y', fontsize=16)\n",
    "    a[1].set_title('Reference Image', fontsize=18)\n",
    "    \n",
    "    point_x = deepcopy(trans_x)\n",
    "    point_y = deepcopy(trans_y)\n",
    "    sse = np.sum((transformed_square - square2)**2)\n",
    "    a[2].bar(0, sse)\n",
    "    a[2].set_ylim([0, 350])\n",
    "    a[2].set_ylabel('SSE', fontsize=18)\n",
    "    a[2].set_xlabel('Cost Function', fontsize=18)\n",
    "    a[2].set_xticks([])\n",
    "    a[2].set_title(f'Parameters: ({int(trans_x)},{int(trans_y)})', fontsize=20)\n",
    "    plt.tight_layout()\n",
    "    \n",
    "interact(plot_affine_cost, \n",
    "         trans_x=FloatSlider(value=0, min=-30, max=0, step=1),\n",
    "         trans_y=FloatSlider(value=0, min=-30, max=0, step=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![costfunction](../images/preprocessing/Cost_Function.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You probably had to move the sliders around back and forth until you were able to reduce the sum of squared error to zero. This cost function increases exponentially the further you are away from your target. The process of minimizing (or sometimes maximizing) cost functions to identify the best fitting parameters is called *optimization* and is a concept that is core to fitting models to data across many different disciplines.\n",
    "\n",
    "| Cost Function | Use Case | Example |\n",
    "|:---:|:---:|:---:|\n",
    "| Sum of Squared Error | Images of same modality and scaling | Two T2* images  |\n",
    "| Normalized correlation | Images of same modality | two T1 images   |\n",
    "| Correlation ratio | Any modality  | T1 and FLAIR   |\n",
    "| Mutual information or normalized mutual information | Any modality  | T1 and CT |\n",
    "| Boundary Based Registration  | Images with some contrast across boundaries of interest | EPI and T1 |\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Realignment\n",
    "\n",
    "Now let's put everything we learned together to understand how we can correct for head motion in functional images that occurred during a scanning session. It is extremely important to make sure that a specific voxel has the same 3D coordinate across all time points to be able to model neural processes. This of course is made difficult by the fact that participants move during a scanning session and also in between runs. \n",
    "\n",
    "Realignment is the preprocessing step in which a rigid body transformation is applied to each volume to align them to a common space. One typically needs to choose a reference volume, which might be the first, middle, or last volume, or the mean of all volumes.\n",
    "\n",
    "Let's look at an example of the translation and rotation parameters after running realignment on our first subject."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 643,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-18T21:26:02.705868Z",
     "start_time": "2020-04-18T21:25:57.688108Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Rotation')"
      ]
     },
     "execution_count": 643,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from bids import BIDSLayout, BIDSValidator\n",
    "import os\n",
    "\n",
    "data_dir = '../data/localizer'\n",
    "layout = BIDSLayout(data_dir, derivatives=True)\n",
    "\n",
    "data = pd.read_csv(layout.get(subject='S01', scope='derivatives', extension='.tsv')[0].path, sep='\\t')\n",
    "\n",
    "f,a = plt.subplots(ncols=2, figsize=(15,5))\n",
    "\n",
    "data.loc[:,['trans_x','trans_y','trans_z']].plot(ax=a[0])\n",
    "a[0].set_ylabel('Translation (mm)', fontsize=16)\n",
    "a[0].set_xlabel('Time (TR)', fontsize=16)\n",
    "a[0].set_title('Translation', fontsize=18)\n",
    "\n",
    "data.loc[:,['rot_x','rot_y','rot_z']].plot(ax=a[1])\n",
    "a[1].set_ylabel('Rotation (radian)', fontsize=16)\n",
    "a[1].set_xlabel('Time (TR)', fontsize=16)\n",
    "a[1].set_title('Rotation', fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't forget that even though we can approximately put each volume into a similar position with realignment that head motion always distorts the magnetic field and can lead to nonlinear changes in signal intensity that will not be addressed by this procedure. In the resting-state literature, where many analyses are based on functional connectivity, head motion can lead to spurious correlations. Some researchers choose to exclude any subject that moved more than certain amount. Other's choose to remove the impact of these time points in their data through removing the volumes via *scrubbing* or modeling out the volume with a dummy code in the first level general linear models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial Normalization\n",
    "There are several other preprocessing steps that involve image registration. The main one is called *spatial normalization*, in which each subject's brain data is warped into a common stereotactic space. Talaraich is an older space, that has been subsumed by various standards developed by the Montreal Neurological Institute.\n",
    "\n",
    "There are a variety of algorithms to warp subject data into stereotactic space. Linear 12 parameter affine transformation have been increasingly been replaced by more complicated nonlinear normalizations that have hundreds to thousands of parameters. \n",
    "\n",
    "One nonlinear algorithm that has performed very well across comparison studies is *diffeomorphic registration*, which can also be inverted so that subject space can be transformed into stereotactic space and back to subject space. This is the core of the [ANTs](http://stnava.github.io/ANTs/) algorithm that is implemented in fmriprep. See this [overview](https://web.archive.org/web/20210613081645/https://elef.soic.indiana.edu/documentation/0.15.0.dev/examples_built/syn_registration_2d/) for more details.\n",
    "\n",
    "Let's watch another short video by Martin Lindquist and Tor Wager to learn more about the core preprocessing steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-18T21:34:30.059504Z",
     "start_time": "2020-04-18T21:34:29.964597Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
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      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7ff8402dc4d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo('qamRGWSC-6g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many different steps involved in the spatial normalization process and these details vary widely across various imaging software packages. We will briefly discuss some of the steps involved in the anatomical preprocessing pipeline implemented by fMRIprep and will be showing example figures from the output generated by the pipeline.\n",
    "\n",
    "First, brains are extracted from the skull and surrounding dura mater. You can check and see how well the algorithm performed by examining the red outline.\n",
    "\n",
    "![normalization](../images/preprocessing/T1_normalization.png)\n",
    "\n",
    "Next, the anatomical images are segmented into different tissue types, these tissue maps are used for various types of analyses, including providing a grey matter mask to reduce the computational time in estimating statistics. In addition, they provide masks to aid in extracting average activity in CSF, or white matter, which might be used as covariates in the statistical analyses to account for physiological noise.\n",
    "![normalization](../images/preprocessing/T1_segmentation.png)\n",
    "\n",
    "### Spatial normalization of the anatomical T1w reference\n",
    "fmriprep uses the [ANTs](http://stnava.github.io/ANTs/) to perform nonlinear spatial normaliziation. It is easy to check to see how well the algorithm performed by viewing the results of aligning the T1w reference to the stereotactic reference space. Hover on the panels with the mouse pointer to transition between both spaces. We are using the MNI152NLin2009cAsym template.\n",
    "![normalization](../images/preprocessing/sub-S01_space-MNI152NLin2009cAsym_T1w.svg)\n",
    "\n",
    "### Alignment of functional and anatomical MRI data\n",
    "Next, we can evaluate the quality of alignment of the functional data to the anatomical T1 image. FSL `flirt` was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL `fast` (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation. Notice these images are much blurrier and show some distortion compared to the T1s. \n",
    "![epi](../images/preprocessing/sub-S01_task-localizer_desc-flirtbbr_bold.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial Smoothing\n",
    "The last step we will cover in the preprocessing pipeline is *spatial smoothing*. This step involves applying a filter to the image, which removes high frequency spatial information. This step is identical to convolving a kernel to a 1-D signal that we covered in the {doc}`../content/Signal_Processing` lab, but the kernel here is a 3-D Gaussian kernel. The amount of smoothing is determined by specifying the width of the distribution (i.e., the standard deviation) using the Full Width at Half Maximum (FWHM) parameter.\n",
    "\n",
    "Why we would want to decrease our image resolution with spatial smoothing after we tried very hard to increase our resolution at the data acquisition stage? This is because this step may help increase the signal to noise ratio by reducing the impact of partial volume effects, residual anatomical differences following normalization, and other aliasing from applying spatial transformation.\n",
    "\n",
    "Here is what a 3D gaussian kernel looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 618,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T04:49:07.720659Z",
     "start_time": "2020-04-17T04:49:07.437484Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_gaussian(sigma=2, kind='surface', cmap='viridis', linewidth=1, **kwargs):\n",
    "    '''Generates a 3D matplotlib plot of a Gaussian distribution'''\n",
    "    mean=0\n",
    "    domain=10\n",
    "    x = np.arange(-domain + mean, domain + mean, sigma/10)\n",
    "    y = np.arange(-domain + mean, domain + mean, sigma/10)\n",
    "    x, y = np.meshgrid(x, x)\n",
    "    r = (x ** 2 + y ** 2) / (2 * sigma ** 2)\n",
    "    z = 1 / (np.pi * sigma ** 4) * (1 - r) * np.exp(-r)\n",
    "\n",
    "    fig = plt.figure(figsize=(12, 6))\n",
    "\n",
    "    ax = plt.axes(projection='3d')\n",
    "    if kind=='wire':\n",
    "        ax.plot_wireframe(x, y, z, cmap=cmap, linewidth=linewidth, **kwargs)\n",
    "    elif kind=='surface':\n",
    "        ax.plot_surface(x, y, z, cmap=cmap, linewidth=linewidth, **kwargs)\n",
    "    else:\n",
    "        NotImplemented\n",
    "    \n",
    "    ax.set_xlabel('x', fontsize=16)\n",
    "    ax.set_ylabel('y', fontsize=16)\n",
    "    ax.set_zlabel('z', fontsize=16)\n",
    "    plt.axis('off')\n",
    "\n",
    "plot_gaussian(kind='surface', linewidth=1)\n"
   ]
  },
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   "source": [
    "(fmriprep)=\n",
    "## fmriprep\n",
    "Throughout this lab and course, you have frequently heard about [fmriprep](https://fmriprep.readthedocs.io/en/stable/), which is a functional magnetic resonance imaging (fMRI) data preprocessing pipeline that was developed by a team at the [Center for Reproducible Research](http://reproducibility.stanford.edu/) led by Russ Poldrack and Chris Gorgolewski. Fmriprep was designed to provide an easily accessible, state-of-the-art interface that is robust to variations in scan acquisition protocols, requires minimal user input, and provides easily interpretable and comprehensive error and output reporting. Fmriprep performs basic processing steps (coregistration, normalization, unwarping, noise component extraction, segmentation, skullstripping etc.) providing outputs that are ready for data analysis. \n",
    "\n",
    "fmriprep was built on top of [nipype](https://nipype.readthedocs.io/en/latest/), which is a tool to build preprocessing pipelines in python using graphs. This provides a completely flexible way to create custom pipelines using any type of software while also facilitating easy parallelization of steps across the pipeline on high performance computing platforms. Nipype is completely flexible, but has a fairly steep learning curve and is best for researchers who have strong opinions about how they want to preprocess their data, or are working with nonstandard data that might require adjusting the preprocessing steps or parameters. In practice, most researchers typically use similar preprocessing steps and do not need to tweak the pipelines very often. In addition, many researchers do not fully understand how each preprocessing step will impact their results and would prefer if somebody else picked suitable defaults based on current best practices in the literature. The fmriprep pipeline uses a combination of tools from well-known software packages, including FSL_, ANTs_, FreeSurfer_ and AFNI_. This pipeline was designed to provide the best software implementation for each state of preprocessing, and is quickly being updated as methods evolve and bugs are discovered by a growing user base.\n",
    "\n",
    "This tool allows you to easily do the following:\n",
    "\n",
    "- Take fMRI data from raw to fully preprocessed form.\n",
    "- Implement tools from different software packages.\n",
    "- Achieve optimal data processing quality by using the best tools available.\n",
    "- Generate preprocessing quality reports, with which the user can easily identify outliers.\n",
    "- Receive verbose output concerning the stage of preprocessing for each subject, including meaningful errors.\n",
    "- Automate and parallelize processing steps, which provides a significant speed-up from typical linear, manual processing.\n",
    "- More information and documentation can be found at https://fmriprep.readthedocs.io/\n",
    "\n",
    "\n",
    "![image.png](../images/preprocessing/fmriprep.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running fmriprep\n",
    "Running fmriprep is a (mostly) trivial process of running a single line in the command line specifying a few choices and locations for the output data. One of the annoying things about older neuroimaging software that was developed by academics is that the packages were developed using many different development environments and on different operating systems (e.g., unix, windows, mac). It can be a nightmare getting some of these packages to install on more modern computing systems. As fmriprep uses many different packages, they have made it much easier to circumvent the time-consuming process of installing many different packages by releasing a [docker container](https://fmriprep.readthedocs.io/en/stable/docker.html) that contains everything you need to run the pipeline.\n",
    "\n",
    "Unfortunately, our AWS cloud instances running our jupyter server are not equipped with enough computational resources to run fmriprep at this time. However, if you're interested in running this on your local computer, here is the code you could use to run it in a jupyter notebook, or even better in the command line on a high performance computing environment.\n",
    "\n",
    "```\n",
    "import os\n",
    "base_dir = '/Users/lukechang/Dropbox/Dartbrains/Data'\n",
    "data_path = os.path.join(base_dir, 'localizer')\n",
    "output_path = os.path.join(base_dir, 'preproc')\n",
    "work_path = os.path.join(base_dir, 'work')\n",
    "\n",
    "sub = 'S01'\n",
    "subs = [f'S{x:0>2d}' for x in range(10)]\n",
    "for sub in subs:\n",
    "    !fmriprep-docker {data_path} {output_path} participant --participant_label sub-{sub} --write-graph --fs-no-reconall --notrack --fs-license-file ~/Dropbox/Dartbrains/License/license.txt --work-dir {work_path}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick primer on High Performance Computing\n",
    "\n",
    "We could run fmriprep on our computer, but this could take a long time if we have a lot of participants. Because we have a limited amount of computational resources on our laptops (e.g., cpus, and memory), we would have to run each participant sequentially. For example, if we had 50 participants, it would take 50 times longer to run all participants than a single one. \n",
    "\n",
    "Imagine if you had 50 computers and ran each participant separate at the same time in parallel across all of the computers. This would allow us to run 50 participants in the same amount of time as a single participant. This is the basic idea behind high performance computing, which contains a cluster of many computers that have been installed in racks. Below is a picture of what Dartmouth's [Discovery cluster](https://rc.dartmouth.edu/index.php/discovery-overview/) looks like:\n",
    "\n",
    "![discovery](../images/preprocessing/hpc.png)\n",
    "\n",
    "A cluster is simply a collection of nodes. A node can be thought of as an individual computer. Each node contains processors, which encompass multiple cores. Discovery contains 3000+ cores, which is certainly a lot more than your laptop!\n",
    "\n",
    "In order to submit a job, you can create a Portable Batch System (PBS) script that sets up the parameters (e.g., how much time you want your script to run, specifying directory to run, etc) and submits your job to a queue.\n",
    "\n",
    "**NOTE**: For this class, we will only be using the jupyterhub server, but if you end up working in a lab in the future, you will need to request access to the *discovery* system using this [link](https://rcweb.dartmouth.edu/accounts/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### fmriprep output\n",
    "You can see a summary of the operations fmriprep performed by examining the .html files in the `derivatives/fmriprep` folder within the `localizer` data directory.\n",
    "\n",
    "We will load the first subject's output file. Spend some time looking at the outputs and feel free to examine other subjects as well. Currently, the first 10 subjects should be available on the jupyterhub."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 621,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T06:08:35.524154Z",
     "start_time": "2020-04-17T06:08:35.517120Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "../data/localizer/derivatives/fmriprep/sub-01.html"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 621,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "\n",
    "HTML('sub-S01.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Limitations of fmriprep\n",
    "In general, we recommend using this pipeline if you want a sensible default. Considerable thought has gone into selecting reasonable default parameters and selecting preprocessing steps based on best practices in the field (as determined by the developers). This is not necessarily the case for any of the default settings in any of the more conventional software packages (e.g., spm, fsl, afni, etc). \n",
    "\n",
    "However, there is an important tradeoff in using this tool. On the one hand, it's nice in that it is incredibly straightforward to use (one line of code!), has excellent documentation, and is actively being developed to fix bugs and improve the overall functionality. There is also a growing user base to ask questions.  [Neurostars](https://neurostars.org/) is an excellent forum to post questions and learn from others. On the other hand, fmriprep, is unfortunately in its current state not easily customizable. If you disagree with the developers about the order or specific preprocessing steps, it is very difficult to modify. Future versions will hopefully be more modular and easier to make custom pipelines.  If you need this type of customizability we strongly recommend using nipype over fmriprep.\n",
    "\n",
    "In practice, it's alway a little bit finicky to get everything set up on a particular system. Sometimes you might run into issues with a specific missing file like the [freesurfer license](https://fmriprep.readthedocs.io/en/stable/usage.html#the-freesurfer-license) even if you're not using it. You might also run into issues with the format of the data that might have some conflicts with the [bids-validator](https://github.com/bids-standard/bids-validator). In our experience, there is always some frustrations getting this to work, but it's very nice once it's done."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "### Exercise 1. Inspect HTML output of other participants.\n",
    "For this exercise, you will need to navigate to the derivatives folder containing the fmriprep preprocessed data `../data/data/localizer/derivatives/fmriprep` and inspect the html output of other subjects (ie., not 'S01'). Did the preprocessing steps works? are there any issues with the data that we should be concerned about?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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