{ "metadata": { "name": "", "signature": "sha256:aedf1fe35e38b0f8a8fb1b39c0d9d69dd01497e502a1464787c6316768ce0245" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "# Compatibility with Python 3\n", "from __future__ import print_function # print('me') instead of print 'me'\n", "from __future__ import division # 1/2 == 0.5, not 0" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# Show figures inside the notebook\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np # the Python array package\n", "import matplotlib.pyplot as plt # the Python plotting package" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using our module" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Later on we are going to use our new module `spm_funcs.py`.\n", "\n", "In order to do this, we need to make sure Python can find the module. Python stores all the directories that it will search for modules in the `path` variable in the `sys` module. We can make sure Python can find our new module by inserting the directory containing the module into `sys.path`.\n", "\n", "Import the `sys` module, and show the `path` variable from the `sys` module." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Show path in sys module\n", "import sys\n", "sys.path" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "['',\n", " '/Users/mb312/dev_trees/pna2015/day4',\n", " '/Users/mb312/tmp/junk/lib64/python2.7/site-packages',\n", " '/Users/mb312/tmp/junk/lib/python2.7/site-packages',\n", " '/Users/mb312/tmp/local/lib64/python2.7/site-packages',\n", " '/Users/mb312/tmp/local/lib/python2.7/site-packages',\n", " '/Users/mb312/usr/lib64/python2.7/site-packages',\n", " '/Users/mb312/usr/lib/python2.7/site-packages',\n", " '/Users/mb312/usr/opt/lib64/python2.7/site-packages',\n", " '/Users/mb312/usr/opt/lib/python2.7/site-packages',\n", " '/Users/mb312/usr/local/lib64/python2.7/site-packages',\n", " '/Users/mb312/usr/local/lib/python2.7/site-packages',\n", " '/local/lib64/python2.7/site-packages',\n", " '/local/lib/python2.7/site-packages',\n", " '/opt/lib64/python2.7/site-packages',\n", " '/opt/lib/python2.7/site-packages',\n", " '/Library/Frameworks/Python.framework/Versions/2.7/lib/python27.zip',\n", " '/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7',\n", " '/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/plat-darwin',\n", " '/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/plat-mac',\n", " '/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/plat-mac/lib-scriptpackages',\n", " '/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-tk',\n", " '/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-old',\n", " '/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-dynload',\n", " '/Users/mb312/Library/Python/2.7/lib/python/site-packages',\n", " '/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages',\n", " '/Library/Python/2.7/site-packages',\n", " '/Users/mb312/tmp/junk/lib64/python2.7/site-packages',\n", " '/Users/mb312/tmp/junk/lib/python2.7/site-packages',\n", " '/Users/mb312/tmp/local/lib64/python2.7/site-packages',\n", " '/Users/mb312/tmp/local/lib/python2.7/site-packages',\n", " '/Users/mb312/usr/lib64/python2.7/site-packages',\n", " '/Users/mb312/usr/lib/python2.7/site-packages',\n", " '/Users/mb312/usr/opt/lib64/python2.7/site-packages',\n", " '/Users/mb312/usr/opt/lib/python2.7/site-packages',\n", " '/Users/mb312/usr/local/lib64/python2.7/site-packages',\n", " '/local/lib64/python2.7/site-packages',\n", " '/local/lib/python2.7/site-packages',\n", " '/opt/lib64/python2.7/site-packages',\n", " '/opt/lib/python2.7/site-packages',\n", " '/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/IPython/extensions']" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Find the directory where you put the `spm_funcs.py` module. For example, for me, that would be `/Users/mb312/pna_code`. Use the `insert` method of the `path` variable to insert this directory at the top of the `path`. Check the help for the `insert` method for how to do this." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Insert the directory containing `spm_funcs.py` at the top\n", "# of sys.path\n", "sys.path.insert(0, '/Users/mb312/pna_code')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# Check you can import spm_funcs\n", "import spm_funcs" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The task time-course" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We've previously had a look at the file `ds114_sub009_t2r1.nii`. This is a 4D FMRI image.\n", "\n", "Now we want to see whether we can detect any signal in that image relating to the task.\n", "\n", "The task was a block design with 10 seconds rest followed by 7 repeats of (30 seconds when the subject thought of verbs followed by 30 seconds rest).\n", "\n", "There's a file in this directory called `ds114_sub009_t2r1_cond.txt` with the task information in it. It comes from subject 9 task 2 and run 1 from OpenFMRI dataset https://openfmri.org/dataset/ds000114 .\n", "\n", "The has one line per \"on\" block, giving the onset time (in seconds), the duration (in seconds) and the amplitude (expected amount of activation for this block).\n", "\n", "Here are the first four lines:\n", "\n", "```\n", "10\t30.000000\t1\n", "70\t30.000000\t1\n", "130\t30.000000\t1\n", "190\t30.000000\t1\n", "```" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Read the file into an array called \"task\".\n", "# \"task\" should have 3 columns (onset, duration, amplitude)\n", "# HINT: np.loadtxt\n", "task = np.loadtxt('ds114_sub009_t2r1_cond.txt')\n", "task.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "(7, 3)" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The TR for this FMRI run was 2.5 seconds. We need to convert the onsets and durations to TRs - so for example the first onset was at 10 seconds, which was the start of TR 4 (10 / 2.5).\n", "\n", "Select out the first two columns of task, and divide by the TR to convert the onset and duration times to be in terms of TRs:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Select first two columns and divide by TR\n", "ons_durs = task[:, :2] / 2.5\n", "ons_durs" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "array([[ 4., 12.],\n", " [ 28., 12.],\n", " [ 52., 12.],\n", " [ 76., 12.],\n", " [ 100., 12.],\n", " [ 124., 12.],\n", " [ 148., 12.]])" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our next step is to make an on-off vector that is 0 when the subject is doing nothing and 1 when the subject is doing the covert verb task.\n", "\n", "The vector will have one value (either 0 or 1) for each TR." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First use nibabel to load the image `ds114_sub009_t2r1.nii`. Check the image shape to find the number of TRs." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Load the image and check the image shape to get the number of TRs\n", "import nibabel as nib\n", "img = nib.load('ds114_sub009_t2r1.nii')\n", "img.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "(64, 64, 30, 173)" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next make a new vector called `time_course` with one entry per TR, with all elements in the vector being zero:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Make new zero vector\n", "time_course = np.zeros(img.shape[-1])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Loop over the rows in the onsets / durations array to give you an onset / duration pair. For each of these pairs, set the matching positions in the `time_course` vector to 1. For example, the first pair will be `4, 12`. For that row, you will want to set `time_course[4]` through `time_course[15]` equal to 1." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Fill in 1s for positions of on blocks in time course\n", "for onset, duration in ons_durs:\n", " time_course[onset:onset + duration] = 1" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the time course:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot the time course\n", "plt.plot(time_course)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "[]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparing task to rest" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a boolean array which is True when `time_course` is 1 and False when `time_course` is 0.\n", "\n", "Make another array that is the opposite - False when `time_course` is 0 and True when time course is 1." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Make two boolean arrays encoding task, rest\n", "is_task_tr = time_course == 1\n", "is_rest_tr = time_course == 0" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now read the image data into an array:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Read the image data into an array.\n", "data = img.get_data()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remember that the 4D array consists of one volume (3D array) per TR.\n", "\n", "We want to select the volumes where the time course is 1 (task volumes). Do this by slicing, using the boolean array you just made." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Create a new 4D array only containing the task volumes\n", "on_volumes = data[..., is_task_tr]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now select the volumes where the time course is 0 (rest volumes):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Create a new 4D array only containing the rest volumes\n", "off_volumes = data[..., time_course == 0]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want to know whether there is a difference in signal in the task volumes compared to the rest volumes. Take the mean over the task volumes and mean over the rest volumes. You should end up with two 3D volumes." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Create the mean volume across all the task volumes\n", "# Then create the mean volume across all the rest volumes\n", "# Hint: remember the `axis` keyword.\n", "on_mean = on_volumes.mean(axis=-1)\n", "off_mean = off_volumes.mean(axis=-1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now subtract the rest mean from the task mean to get a difference volume." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Create a difference volume\n", "difference = on_mean - off_mean" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Show a slice over the third dimension of the difference volume, from somewhere around the center of the third axis:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Show a slice over the third dimension\n", "plt.imshow(difference[:, :, 14], cmap='gray')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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pt+rtNy70nFvPUIKEb2NjY1iGu7y8HOfn57G+vj5E2C8uLoY2cD7/7OwsTk9P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\nu2VlM1jtF9EZBX8RniM9s8BkJvxTKGHRdK+WPtO4+s48XHZIS+HluRJ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"text": [ "" ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the difference between activation and rest. It looks a little strange. Maybe there are some artefacts here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fixing an artefact" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's use our `spm_globals` function to see if there are any volumes with odd values for this estimate of the mean." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the `get_spm_globals` function in `spm_funcs` to get the SPM global values for each volume in `ds114_sub009_t2r1.nii`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Get SPM global values for each volume\n", "spm_globals = spm_funcs.get_spm_globals('ds114_sub009_t2r1.nii')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot the SPM global values for each volume\n", "# Try using 'o' marker symbol to make it easier to see, as in\n", "# plt.plot(variable, 'o')\n", "plt.plot(spm_globals, 'o')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "[]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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/HaXxDq6XX36FvXuP4sCB0wg6+qJhd4XOvEmTnmflyk+ldiylDWkNruCt7xXL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\n", "text": [ "" ] } ], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is one volume that is a large outlier in the SPM globals.\n", "\n", "Slice out the bad volume (to a 3D array) and the following volume.\n", "\n", "Use `plt.subplot` to show a matching slice from the bad volume and the following volume side-by-side:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Select bad volume and following volume\n", "# Use subplot to show slice from good and next\n", "bad_vol = data[..., 0]\n", "next_vol = data[..., 1]\n", "plt.subplot(1, 2, 1)\n", "plt.imshow(bad_vol[:, :, 14], cmap='gray')\n", "plt.subplot(1, 2, 2)\n", "plt.imshow(next_vol[:, :, 14], cmap='gray')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 22, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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7fHzcs2qSEQLq5OQkpqamvJ5rXkdHRzg4OMDJycmA/lqQ5d8cDLSs7A9qaWo52U9OTk4G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\nYfUwNOeW972xyVAtSx36fCPD+kISqsDrTqssk83zR97kybSqZasTVOu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"text": [ "" ] } ], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you might be able to see, the bad volume is one of the rest volumes.\n", "\n", "Use your slicing skills to remove this volume from your selection of rest volumes." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Use slicing to remove outlier volume from rest volumes\n", "off_volumes_fixed = off_volumes[..., 1:]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a new mean for the rest volumes, and subtract this mean from the mean for the task volumes to make a new difference image.\n", "\n", "Give the new difference image a new name, so we can compare to the old difference image later." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Make new mean for rest volumes, subtract from task mean\n", "off_mean_fixed = off_volumes_fixed.mean(axis=-1)\n", "difference_fixed = on_mean - off_mean_fixed" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use subplot to show the same slice from the old difference volume and the new difference volume, side by side:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Use subplot to show same slice from old and new difference volume\n", "plt.subplot(1, 2, 1)\n", "plt.imshow(difference[:, :, 14], cmap='gray')\n", "plt.subplot(1, 2, 2)\n", "plt.imshow(difference_fixed[:, :, 14], cmap='gray')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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QXyp2P9f73WXCkX5SWMhlziHk+ujoqKTXgdRPT091eHiog4MDnZ6eanJyUlNT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