{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "A simple second level t-test using nipy" ] }, { "cell_type": "heading", "level": 2, "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Simple random effect model" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Random effect model in neuroimaging refer to the fact that the variance used for testing the effect is the between subject variance. In other words, we take the effect computed for each subject (the contrast maps) and we enter these in a second level effect model." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# try Bertrand's and Alexis example on Matthew's data : \n", "import numpy as np\n", "import nibabel as nib\n", "from nibabel import Nifti1Image as Image\n", "import nipy\n", "#from nipy.utils import example_data\n", "\n", "print nipy.__file__ # see which nipy we are using" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "/home/jb/.local/lib/python2.7/site-packages/nipy/__init__.pyc\n" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Get the data located in ./ds107/analysis/sub0*.img" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import os\n", "import os.path as osp\n", "from os.path import join as pjoin, isfile\n", "import zipfile as zpf \n", "\n", "#----- SET THE DIRECTORY WHERE YOUR DATA SIT ------# \n", "DATADIR = pjoin(osp.expanduser('~'), 'data', 'ds107')\n", "\n", "#----- if DATA ARE ZIP : UNZIP --------------------#\n", "datazip = pjoin(pjoin(DATADIR,'analysis.zip')) \n", "if osp.isfile(datazip):\n", " with zpf.ZipFile(datazip, \"r\") as z:\n", " z.extractall(DATADIR)\n", "\n", "list_subj = range(1,11)\n", "dsubj = [pjoin(DATADIR,'analysis','sub' + \"%03d\" % i) for i in list_subj]\n", "f_cnames = [pjoin(dsubj[i-1],'copes','_model_id_1cope01.nii.gz') for i in list_subj]\n", "f_vnames = [pjoin(dsubj[i-1],'varcopes','_model_id_1varcope01.nii.gz') for i in list_subj]\n", "\n", "#fnames = [os.path.join(scans_dir, 'snn03055dy%i.img' % i) for i in range(1,13)]\n", "%pwd\n", "print [ (f, isfile(f)) for f in f_cnames]\n", "\n", "# keep only images that are on disk !\n", "f_cnames = [f for f in f_cnames if isfile(f)]\n", "f_vnames = [f for f in f_vnames if isfile(f)]" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[('/home/jb/data/ds107/analysis/sub001/copes/_model_id_1cope01.nii.gz', True), ('/home/jb/data/ds107/analysis/sub002/copes/_model_id_1cope01.nii.gz', True), ('/home/jb/data/ds107/analysis/sub003/copes/_model_id_1cope01.nii.gz', True), ('/home/jb/data/ds107/analysis/sub004/copes/_model_id_1cope01.nii.gz', True), ('/home/jb/data/ds107/analysis/sub005/copes/_model_id_1cope01.nii.gz', True), ('/home/jb/data/ds107/analysis/sub006/copes/_model_id_1cope01.nii.gz', True), ('/home/jb/data/ds107/analysis/sub007/copes/_model_id_1cope01.nii.gz', True), ('/home/jb/data/ds107/analysis/sub008/copes/_model_id_1cope01.nii.gz', True), ('/home/jb/data/ds107/analysis/sub009/copes/_model_id_1cope01.nii.gz', True), ('/home/jb/data/ds107/analysis/sub010/copes/_model_id_1cope01.nii.gz', False)]\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "c_images = [nib.load(f) for f in f_cnames if isfile(f)]\n", "v_images = [nib.load(f) for f in f_vnames if isfile(f)]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "print len(c_images), len(v_images)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "9 9\n" ] } ], "prompt_number": 4 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Computing the mask of our images - within and between subjects" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from nipy.labs.mask import compute_mask, compute_mask_files\n", "\n", "# first try with the contrast image: \n", "image = c_images[0]\n", "\n", "def plot_mask_image(image):\n", " data_img0 = image.get_data()\n", " aff0 = image.get_affine()\n", " hdr0 = image.get_header()\n", " mask_arr = compute_mask(data_img0, None, m=0.2, M=0.9, \\\n", " cc=True, opening=2, exclude_zeros=False)\n", "\n", " mask_img = nib.Nifti1Image(mask_arr, aff0, hdr0)\n", "\n", " f, (a1, a2) = subplots(1,2,figsize=(10,3))\n", " a1.imshow(data_img0[:,:,12])\n", " a1.set_title('my data example')\n", " a2.imshow(mask_arr[:,:,12])\n", " a2.set_title('my mask example')\n", " \n", "plot_mask_image(image)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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ffGMM5GrsDg7HBlXabGU3W9jDbraym60sMvm0jtmjxG620qdojribOqscZIZH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CSV2b2XUOehP1wWiaYCSaPcmHq33Nbs60TrNy9x7WgXE62l1ITJvu3YA4N2fc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\nHRSmI91r/MO0hIOCbFrCd/tHnu88fW4wWp5SQ9vcAj2tIO1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"text": [ "" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "the_data = image.get_data()\n", "the_data.shape\n", "#plt.hist(the_data.ravel())\n", "plt.hist(np.log(the_data.ravel() + 1),50)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "(array([86377, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 32, 494, 1763, 2199, 5108, 14758, 13586,\n", " 7726, 4322, 2546, 1570, 1009, 639, 432, 286, 197,\n", " 121, 69, 32, 31, 16, 22, 7, 4, 3,\n", " 4, 3, 2, 1, 1]),\n", " array([ 0. , 0.25445147, 0.50890293, 0.7633544 ,\n", " 1.01780586, 1.27225733, 1.52670879, 1.78116026,\n", " 2.03561172, 2.29006319, 2.54451466, 2.79896612,\n", " 3.05341759, 3.30786905, 3.56232052, 3.81677198,\n", " 4.07122345, 4.32567492, 4.58012638, 4.83457785,\n", " 5.08902931, 5.34348078, 5.59793224, 5.85238371,\n", " 6.10683517, 6.36128664, 6.61573811, 6.87018957,\n", " 7.12464104, 7.3790925 , 7.63354397, 7.88799543,\n", " 8.1424469 , 8.39689837, 8.65134983, 8.9058013 ,\n", " 9.16025276, 9.41470423, 9.66915569, 9.92360716,\n", " 10.17805862, 10.43251009, 10.68696156, 10.94141302,\n", " 11.19586449, 11.45031595, 11.70476742, 11.95921888,\n", " 12.21367035, 12.46812181, 12.72257328]),\n", " )" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Usually, you will like to use all subjects (for instance the mean across subjects) to compute the mask" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print f_vnames" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['/home/jb/data/ds107/analysis/sub001/varcopes/_model_id_1varcope01.nii.gz', '/home/jb/data/ds107/analysis/sub002/varcopes/_model_id_1varcope01.nii.gz', '/home/jb/data/ds107/analysis/sub003/varcopes/_model_id_1varcope01.nii.gz', '/home/jb/data/ds107/analysis/sub004/varcopes/_model_id_1varcope01.nii.gz', '/home/jb/data/ds107/analysis/sub005/varcopes/_model_id_1varcope01.nii.gz', '/home/jb/data/ds107/analysis/sub006/varcopes/_model_id_1varcope01.nii.gz', '/home/jb/data/ds107/analysis/sub007/varcopes/_model_id_1varcope01.nii.gz', '/home/jb/data/ds107/analysis/sub008/varcopes/_model_id_1varcope01.nii.gz', '/home/jb/data/ds107/analysis/sub009/varcopes/_model_id_1varcope01.nii.gz']\n" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "# you can also compute the mask from the files names (or list of files) directly\n", "mask_arr, mean_img = compute_mask_files(f_vnames, output_filename=None, \\\n", " return_mean=True, m=0.2, M=0.9, cc=1, \\\n", " exclude_zeros=False, opening=2)\n", "\n", "mask_img = nib.Nifti1Image(mask_arr, image.get_affine(), image.get_header())" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "# print type(mask_arr)\n", "f, (a1, a2) = subplots(1,2,figsize=(10,3))\n", "a1.imshow(mean_img[:,:,12])\n", "a1.set_title('my_example')\n", "a2.imshow(mask_arr[:,:,12])\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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QSY1Mcmrqb7eOfFPtl/spaW2tA3SRGD1+aE1ETPIaR6myEAW2Cl0daMbQNHZD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\nZQWD5BmjbcZ7eitS56IOydi6BKm1pSc+7nEotkK5FVgB5ZXgOqIV3zK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"text": [ "" ] } ], "prompt_number": 10 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Construct an \"interesting\" model:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "image = c_images[0]\n", "images = c_images\n", "# transformation from voxel no to mm\n", "M = image.get_affine()\n", "# mm to voxel no\n", "iM = np.linalg.inv(M)\n", "# coordinates of voxel of interest in mm (MNI space)\n", "posmm = [-20.0, -42, 34]\n", "# coordinates in voxel space (in homogenous coordinates)\n", "posvox = np.dot(iM, posmm + [1])\n", "# We grab the spatial part of the output. Since we want to use it as an \n", "# index, we need to make it a tuple\n", "posvox = tuple(posvox[:3].astype(int))\n", "\n", "print posvox\n", "\n", "nimgs = len(images)\n", "vdata = np.zeros(nimgs)\n", "gdata = np.zeros(nimgs)\n", "\n", "for i, V in enumerate(images):\n", " d = V.get_data()\n", " vdata[i] = d[posvox]\n", "\n", "s = np.std(vdata)/20\n", "x1 = vdata + np.random.randn(vdata.shape[0])*s\n", "print x1\n", "\n", "X = np.hstack((vdata[:,newaxis], ones((len(images),1))))\n", "print X" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(24, 20, 28)\n", "[ 1.81766892 -62.5359811 23.47866984 -42.77734432 13.09622161\n", " -28.95755905 11.14181709 -17.1068815 -9.37745997]\n", "[[ 1.66273332 1. ]\n", " [-62.3576088 1. ]\n", " [ 24.13196373 1. ]\n", " [-42.97476196 1. ]\n", " [ 13.57378292 1. ]\n", " [-31.6486969 1. ]\n", " [ 12.97807789 1. ]\n", " [-17.6577549 1. ]\n", " [ -9.78118229 1. ]]\n" ] } ], "prompt_number": 11 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Checking that all files have the same affine :" ] }, { "cell_type": "code", "collapsed": false, "input": [ "aff0 = v_images[0].get_affine()\n", "shape0 = v_images[0].shape\n", "\n", "for i,img in enumerate(v_images): \n", " if i < 2: # print only ..\n", " print img.get_data().shape\n", " print img.get_affine()\n", " print \"same as the first one ?: \", np.allclose(aff0, img.get_affine())\n", " \n", "#go from a list of 3d to one 4d image:\n", "data_images_4d = nib.concat_images(c_images, check_affines=False)\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(64, 64, 35)\n", "[[ 3. 0. 0. -93. ]\n", " [ 0. 3. 0. -103.55609131]\n", " [ 0. 0. 3. -51.73400116]\n", " [ 0. 0. 0. 1. ]]\n", "same as the first one ?: True\n", "(64, 64, 35)\n", "[[ 3. 0. 0. -93. ]\n", " [ 0. 3. 0. -109.034729 ]\n", " [ 0. 0. 3. -76.77787018]\n", " [ 0. 0. 0. 1. ]]\n", "same as the first one ?: False\n" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "from nipy.modalities.fmri.glm import FMRILinearModel\n", "#data_images = [nib.load(f) for f in f_cnames]\n", "\n", "# FMRILinearModel will \n", "# * load the data in fmri_model.fmri_data\n", "# * load X in fmri_model.design_matrices\n", "# * load the mask or compute it from the runs\n", "fmri_model = FMRILinearModel(data_images_4d, X, mask_img)\n", "\n", "# GLM fitting using ordinary least_squares\n", "fmri_model.fit(do_scaling=False, model='ols')\n", "\n", "# Here, we were doing the first level analysis, we would generally leave this unspecified, \n", "# and the fit would do a first pass with model=\"ar1\", whiten the data, then a second pass\n", "# with model = 'ols'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "# specify and estimate the contrast\n", "contrast_val = np.array(([[1, 0]])) # \n", "print contrast_val\n", "\n", "# By default would do a t-test\n", "z_map, = fmri_model.contrast(contrast_val, contrast_type='t', con_id='', output_z=True)\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[1 0]]\n" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "from nipy.utils import templates, DataError\n", "\n", "templates" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "# look at the result\n", "from nipy.labs.viz import plot_map, cm\n", "import matplotlib.pyplot as plt\n", "#anat = \n", "\n", "vmax = max(- z_map.get_data().min(), z_map.get_data().max())\n", "vmin = - vmax\n", "plot_map(z_map.get_data(), z_map.get_affine(), #anat=anat,\n", " cut_coords=(-20.0, -42, 34),\n", " cmap=cm.cold_hot,\n", " vmin=vmin,\n", " vmax=vmax,\n", " threshold=1.5,\n", " black_bg=True)\n", "#plt.show()\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/home/jb/.local/lib/python2.7/site-packages/nipy/labs/datasets/volumes/volume_img.py:284: FutureWarning: Numpy has detected that you (may be) writing to an array returned\n", "by numpy.diagonal or by selecting multiple fields in a record\n", "array. This code will likely break in the next numpy release --\n", "see numpy.diagonal or arrays.indexing reference docs for details.\n", "The quick fix is to make an explicit copy (e.g., do\n", "arr.diagonal().copy() or arr[['f0','f1']].copy()).\n", " pixdim[0] = -pixdim[0]\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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TcOfnkXzoIRQGB+EtXQf1SwZE8lr0tKu83iBWWab8yA7ItASZinw+j+npabRa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2Esms0BtZqL5yU4ciTJ0aOzw1EOqNfrY7QWcRwyS0usPvkkmZkZosp6pveOpsewZn5u7itpjUab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"text": [ "" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "By looking at the object, we see a number of useful options:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# do something like\n", "fmri_model.contrast??" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "F contrast can be handled the same way. If you have several lines on your contrast, by defaut this will be treated as an F-test. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "# For instance :\n", "contrast_val = np.array(([ [[1, 0],[0, 1]] ])) #" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are some:\n", " ....\n", "\n", "- output_z : bool, optional\n", " Return or not the corresponding z-stat image\n", "- output_stat : bool, optional\n", " Return or not the base (t/F) stat image\n", "- output_effects : bool, optional\n", " Return or not the corresponding effect image\n", "- output_variance : bool, optional\n", " Return or not the corresponding variance image\n" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Mixed effect." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Mixed effect models refer to the situation when in a model some of the effects are random, and some are fixed. In neuroimaging, the issue is that there is two variances to think of. \n", "\n", "* The intra run variance\n", "* The inter subject variance.\n", "\n", "The problem:\n", "------------\n", "\n", "When you want to estimate the variance between subject, you are dealing with noisy observation: the total variance has to be decomposed. We often have a good idea of the first level variance, because in general there are many scans per run, but still there's no direct formula to get the intersubject variance." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For one voxel, let's $Y$ be the observed effects (a vector of $n$-subjects components)\n", "\n", "As our $Y$ are measured with a know variance, we have\n", "\n", "$$\n", "Y = Z + \\epsilon_1\n", "$$\n", "\n", "where the $Z_i$ represents the \"true\" measurement for suject $i$, $\\epsilon_1 \\sim N(0,\\Sigma)$, with $\\Sigma = diag(\\sigma_i^2)$\n", "\n", "and at the group level, we have a model between subjects for the $Z$ : \n", "\n", "$$ \n", "Z = X\\beta + \\epsilon_2\n", "$$\n", "\n", "where $\\beta$ is the group effect, and $\\epsilon_2 \\sim N(0,vI)$, $v$ being the group variance (subjects are assumed to be independant).\n", "\n", "Overall, we have \n", "\n", "$\\mathrm{var}(Y) = \\Sigma + vI_n $ \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Most of good packages now include a way of dealing with this. While it is possible to estimate simultaneously $v$, $\\Sigma$, and $\\beta$ , this requires lenghty computations, and we generally have a good confidence on the $\\sigma_i$, such that they are assumed known after a first level estimation, so that only $v$ and $\\beta$ remains to be estimated.\n", "\n" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "here - an explanation of the EM ?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nipy has a couple of routines for this. Let's examine one with a simple example. First create an array of data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "n_sub = 15 # the number of subjects\n", "n_vox = 77 # the number of voxels\n", "\n", "# inter subject variance\n", "V2 = np.arange(1,2,1.0/n_sub)\n", "V2 = V2[:,np.newaxis]\n", "vox_variance = np.arange(1.,10.,9.0/n_vox)\n", "vox_variance = vox_variance[np.newaxis,:]\n", "V2 = np.dot(V2, vox_variance)\n", "\n", "\n", "# within subject variance > within variance\n", "V1 = .5*np.ones(V2.shape) \n", "print np.max(V1)\n", "\n", "\n", "# check that the variances are positive\n", "if (V1 < 0).any():\n", " raise ValueError('Variance should be positive')\n", "\n", "Y = np.random.standard_normal(V1.shape)\n", "Y *= np.sqrt(V2 + V1)\n", "\n", "X = np.ones(n_sub)\n", "if X.ndim == 1:\n", " X = X[:, np.newaxis]\n", "\n", "# put enough signal such that we should see something significant\n", "beta = 1.\n", "if np.isscalar(beta):\n", " beta = beta * np.ones((X.shape[1], V1.shape[1]))\n", "if beta.ndim == 1:\n", " beta = beta[np.newaxis]\n", "\n", "Y += np.dot(X, beta)\n", "\n", "print np.max(Y)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.5\n", "10.8152721104\n" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "from nipy.algorithms.statistics.mixed_effects_stat import mfx_stat \n", "# column to be tested in X\n", "column = 0 \n", "t, varh, betah = mfx_stat(Y, V1, X, column, n_iter=5, return_t=True, return_f=False, return_effect=True, \\\n", " return_var=True, verbose=False)\n", "\n", "print t.shape, betah.shape, varh.shape\n", "\n", "print sum(t>2.)\n", "\n", "fig = plt.figure()\n", "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharex=True, figsize=(12,4))\n", "\n", "ax1.plot(t)\n", "ax2.plot(betah)\n", "ax3.plot(varh)\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(77,) (77,) (77,)\n", "20\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "[]" ] }, { "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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QPnMmWCLSjSMc1Ew/w/iF7Aino50KkcoLaiSZjg7K4NbWWufZ/MgIA/FCuKuL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"text": [ "" ] } ], "prompt_number": 20 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Calculate significant voxels and clusters : see Multiple comparison notebook" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }