{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Properly Transforming T to Z Scores for Large Brain Maps\n", "\n", "We discovered a strange truncation of strongly negative values when converting from T statistic scores --> P values --> Z scores. First we will show the strangeness. \n", "\n", "### The task behind the map\n", "This is a group map for a \"story\" contrast from a language task from the Human Connectome Project (HCP). For [this task](http://www.sciencedirect.com/science/article/pii/S1053811913005272), there are alternating blocks of doing match problems and listening to a story. This contrast is for the \"story\" blocks.\n", "\n", "### The map\n", "We concatenated each single subject cope1.nii.gz image representing this contrast in time, for a total of 486 subjects (timepoints), and ran randomise for 5000 iterations (fsl)." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "randomise -i OneSamp4D -o OneSampT -1 -T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Viewing the T Statistic Map\n", "\n", "Now we can read in the file, and first look at the image itself and the T-distribution." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib\n", "import matplotlib.pylab as plt\n", "import numpy as np\n", "%matplotlib inline\n", "import nibabel as nib\n", "from nilearn.plotting import plot_stat_map, plot_roi\n", "from scipy.spatial.distance import pdist\n", "from scipy.stats import norm, t\n", "import seaborn as sns\n", "\n", "all_copes_file = \"/home/vanessa/Desktop/tfMRI_LANGUAGE_STORY.nii_tstat1.nii.gz\"\n", "all_copes = nib.load(all_copes_file)\n", "plot_stat_map(all_copes)\n", "print \"Here is our map created with randomise for all 486 subjects, for the story contrast\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Here is our map created with randomise for all 486 subjects, for the story contrast\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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p66JFrNXGj0FfJ5WBkKDZznPejURotg8JQ0FGR5mevKW8AkO5psSK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\nMD20nb+Vno1WtbICNOHicalFP1ZFhS5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KbQxFF3utUzVJDsdCgjM9DofD4Tgg6EBwGGZo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\nha997Wuz0FLHfMBXv/pVnHHGGejt7cUrXvGKdP1PfvELPN5qodV6PPz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"text": [ "" ] } ], "prompt_number": 89 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now I want to point out something about this map - we have a set of strongly negative outliers." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Function to flag outliers\n", "def plot_outliers(image,n_std=6):\n", " mr = nib.load(image)\n", " data = mr.get_data()\n", " mean = data.mean()\n", " std = data.std()\n", " six_dev_up = mean + n_std * std\n", " six_dev_down = mean - n_std*std\n", " empty_brain = np.zeros(data.shape)\n", " empty_brain[data>=six_dev_up] = 1\n", " empty_brain[data<=six_dev_down] = 1 \n", " outlier_nii = nib.nifti1.Nifti1Image(empty_brain,affine=mr.get_affine(),header=mr.get_header())\n", " plot_roi(outlier_nii)\n", "\n", "plot_outliers(all_copes_file)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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9pqhXBONRIlaubK4j0FRXJqrlYfhMb+o3EVUA5e9Ap+cDa7P4WUMLt5/Lfn+k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\nC8mYL33pS5KqGgXlWHiMKBWsqc8GA+dsUYjwOmB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"text": [ "" ] } ], "prompt_number": 179 }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is a separate problem entirely if those outliers should be there, but for the purposes of this problem, we would want any conversion from T to Z to maintain those outliers. Let's now look at the distribution of the data.\n", "\n", "# Viewing the T Distribution" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = all_copes.get_data()\n", "data = data[data!=0]\n", "sns.distplot(data.flatten(), label=\"Original T-Stat Data\")\n", "plt.legend()\n", "print \"Here is our map created with randomise for all 486 subjects, for the story contrast\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Here is our map created with randomise for all 486 subjects, for the story contrast\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have a heavy left tail, meaning lots of strongly negative values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Converting from T to P-Values\n", "\n", "We next will convert T scores into P values by way of the \"survival function\" from the scipy.stats t module. The survival function is actually 1 - the cumulative density function (CDF) that will give us the probability (p-value) for each of our random variable (the T). \n", "\n", "![this](http://upload.wikimedia.org/math/d/b/1/db1695bdb2b59b9bd2d9d818b9a3b505.png)\n", "\n", "The degrees of freedom should be the number of subjects from which the group map was derived -2." ] }, { "cell_type": "code", "collapsed": false, "input": [ "dof=486 - 2 \n", "data = all_copes.get_data()\n", "p_values = t.sf(data, df = dof)\n", "p_values[p_values==1] = 0.99999999999999\n", "sns.distplot(p_values.flatten(), label=\"P-Values from T-Stat Data\")\n", "plt.legend()\n", "print \"Here are the p-values created from the t-stat map, including all zeros in the map when we calculate\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Here are the p-values created from the t-stat map, including all zeros in the map when we calculate\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 96 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Converting from P-Values to Z-Scores\n", "Now we can use scipy.stats.norm inverse survival function to \"undo\" the p-values back into normal (Z scores)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "z_values = norm.isf(p_values)\n", "sns.distplot(z_values.flatten(), label=\"Z-Values from T-Stat Data\")\n", "plt.legend()\n", "print \"Here are the z-values created from the t-stat map, including all zeros in the map when we calculate\"\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Here are the z-values created from the t-stat map, including all zeros in the map when we calculate\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 93 }, { "cell_type": "markdown", "metadata": {}, "source": [ "But now we see something strange. The distribution looks almost truncated. When we look at the new image, the strong negative values (previously outliers in ventricles) aren't there either:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Need to make sure we look at the same slices :)\n", "def plot_outliers(image,cut_coords,n_std=6):\n", " mr = nib.load(image)\n", " data = mr.get_data()\n", " mean = data.mean()\n", " std = data.std()\n", " six_dev_up = mean + n_std * std\n", " six_dev_down = mean - n_std*std\n", " empty_brain = np.zeros(data.shape)\n", " empty_brain[data>=six_dev_up] = 1\n", " empty_brain[data<=six_dev_down] = 1 \n", " outlier_nii = nib.nifti1.Nifti1Image(empty_brain,affine=mr.get_affine(),header=mr.get_header())\n", " plot_roi(outlier_nii,cut_coords=cut_coords)\n", "\n", "Z_nii = nib.nifti1.Nifti1Image(z_values,affine=all_copes.get_affine(),header=all_copes.get_header())\n", "nib.save(Z_nii,\"/home/vanessa/Desktop/Zimage.nii\")\n", "plot_outliers(\"/home/vanessa/Desktop/Zimage.nii\",cut_coords=(7,0,13))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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K8+rfvkb6gKOIcFbDi/zcjJGI+o7UKQreF+carXT6Z6MqhFFdDaokjP3h9pnZ\nUVdfw9tqlaXgV6qGVBbYw8HWG4+Z9wBrrdTVBIkUGF47W8pD0WVy4cKFkqrqhTwP7C3D+VPC2gH+\nrmuFWFGgisP7POo/wk6cVC74zGplMff33DWRksD7hRVOuY+oAi7vHdNKUWTMTKkosPqqn4GsAktV\nYqhJRSFJkiRJkpCuUBQYXU5FgdHiUnPHvahSIGMZWHs+iuKOrPwob7jud7Qg3THNq3VGuJqyAuCK\nFSs0duxYvffee011E9qtBFY3tgjGDdASiOrIR5Hkvg5lFzReI79GdSGGAsYmUFEorQuqR+wxYL+h\nr18rZYE9QHxO+X2fI/qUWWmwzvdKK4aKEGE0e7uKQbv53NzekiVLGo6h7l7mtWAsQtTPJfI5R7no\njFeixUlfd90zIspIoTpRV6djsLg6IaP6qaKyDwfHVo6Pfvnoevva+FpRJeWzOrLmWfGxnfo0/f28\nhKpWVEGRMW9RVdoovotKVdRxl5+j2ipV9ymfR7znrSSUnYWHklQUkiRJkiQJ6QpFIbIk+ltlRjmu\n0WoxsoSjfOKIKL+4/G7ko2bNBuZnMz5ggw020NixY9XT09NgkdDi5bGVxxRF9kbQ1xp1RGvVGyOy\nUKRm9YexKMOBz61X5LT+6/zPrJxJNcKv/pwtVUabcz773DAjh75Rdkk0dfE1rCPCc0t1JHo1zEDg\nMdF/G80z/95ZAKzPIVUKG5UUv0YVBlvNwVaZIKwt4XuM9yOrwJY/i6L9WS/AtULmz58vSTruuOM0\nUDg+ZuMwS8Qwy6Y8Nr5nDEJUmZP9Eni9+Xn66f17XpsTTzyx9tjrYlqIs4aiWg79Vaosv8ex8rlE\nNcTngPOY59J1Fsr98toxZooZOO0oK4MhFYUkSZIkSUK6QlGgTzeyFMrVFC0oWgsmiqCmL42r0Mjy\nZm5s2fv8hRdekFRVG3NPdSoHzO8m5fGWflmvZB3D4P3ZSvb+Sj8Wjy/yv7XKghhorXFur4xaphrD\na/P3f//3kqQzzjhjQPus45577pHU3Gmvv/gOjjmq6W6LjvnRUV8Ddq9jDEurTqVUZ0rLkdUKo2wG\nWrpRRHhUDyOyxqJ71Z939D/VFKlSZGyRMZYnUhgiP7ehH934/Hn7tJI5dio/UmXpsTYG/dGMe7BV\nmSSdRFcsFJIkSZJ6rrjiCknS7rvv3vBzFpLyQpTBjFyolr+j9G9apVFHBe+Y4sq0Si52vf3SFVWH\njazLLrtizrcbAAAgAElEQVSs72dsIGXXA4OCuXijGyNyw9ElygUlUxv9ninjNtx8neyGK/dFVwyL\nr/n80K00VHT0QsGV+HbbbTdJravDlXACR1YqLzKVhKgSIV/p3/J+yowF91Z/8sknJUmTJ0+WJG2/\n/faSpAkTJkhqtuYiPvzwQ/X29qq3t1dLly7Vs88+K6nq3e7ve/ueRP1ZyTw/hL7GVjEIgyGKXveD\nbCh7r0dZLoyhKOHvbElyvIzW983MucIMnVaKAsfEWv58QJXbph87ityPrmdU7TDKeqDyRMs6UvjK\nP1r+v6P5/ZBcunSppEpBs/LgY4zUIY6JcRpRZhSzIeinL6+LtxHVH/Hxcx9DEauQJENNRy8UkiRJ\nkv5hcSQvOuw68aKF7g0aDOXC0osuWqp+pWslcoNxIcgUYMPiVdy+jyXCi0O6kUq4yPUiLSqwFLlH\nuRhm6X2WI48Kffm6MSiyLDDIgldvvPGGpGZjwJ+zOnHVVVdJko499tjwfAyEjl4oRNX8Ir9qSWTR\n0rKJMim4nahLpGF0MyPhJWmHHXaQJD311FOSpCeeeEJS1Td90aJFkqSpU6dKqhQGW1KecO65vnjx\nYi1btkwrVqzQfffd13cz+XvenzvW0TIqj5vjj6Jo2623QCL/Lv365Wej3P+hUBQcm8D4kFZ53OX4\nWPufef3eth/MPu+2iKPqc1S3mP8e1TjwOPw5zxupmgOUR1u9esy/+93vJEkvvfSSpCpOgJ3yOLbT\nTz9ddbgSobf/2GOPSarid0pfPaVhW91+KFpRsMLgsbJGAdVDZjgxfsP79UPaXUF9fqkYlbUfOIf5\nWaoZ3pePabjy4ZNkMHT0QiFJkiRZybRp02p/bus/KvkdLdL5udJ1Ei0cI1qlwZrI8CBRQbyzzjqr\n4XNsvFbu75RTTpFULWq//e1vS2qdusuxtToHURpluw306Gotx0O3VmTIMWbEC9Bzzz23abzPPPNM\n7XH0R0cvFKJ+4ZGyUMLPMmd9oBM7yn6I8qRNKbHZwnPMAGWyI488suH9JZdcIknaZZddJEkvv/yy\nJOmwww7r+4xvHN8UknTfffc17I/nrzwGjiF6yETd3aLugpHyEHWPq+vTEVl47eRMtwsDmqIskPKc\nUcKMKgWyNgOtcz/kovncqoY8MwT8agu6DIpqpRzxwWwrffHixZKqyolWrXjdB1qPwz0NzjnnHEnS\npz/96Ybf33LLLX3/33bbbSVVWTvsUGgFzQqC/zBQAfB5YU+MKOaAx2AlyOeA2Vil0kUZm0oOlRde\n66Go1JgkQ0VHLxSSJEmSlUSWoN1oW265paRqkcEIeS/u6qLrpcqtJFWLLbtzWBSILjsbJXaxeFFM\nS5hupCgYmj55G0nPPfdcw/fdAGurrbZq2K9UBXXPnDlTknTzzTdLqhaWLOQXWfYs7c3MDS7kWXrf\nC1YrP74ePHceV2k0ecHORa/3yYwW78OB85/73Oc0FHT0QoHR4dGF7K8qVasqjr6ZaL1SeYj2EVkf\ndV0o2ZlwypQptcdtrBLccMMNkqqbJWLevHmSpF133bVhf4y8LqFlGJ0v9mTwjeybgVYVA6xMO5UZ\nDSXCuniGwcKOlFHWSl2uPXsDMMqeAV0evz/HWAVmQ0Rd6WilMqPH+/XDvFQgIgufFq5VDj+YfAyO\ncXB8QKtukj5f1113XcP22PUvYsaMGU0/832w0047Sar+MNK/z66IVnD8x8+fi7qRtvojxj+8Pnel\nosBANfaVMNF887aGOigtSQZDRy8UkiRJkv7xgqfVgtrWphctXrDWLby5SGKDIwZdtnIHtopNoIvP\nx2Jrm/v1gpYNA8s6AgxiteuqVQo0F7+tGlNF2SNs5ub9+Hr51eNkymw5FraX5jWm29PGQtSWe6B0\n9EKBF4zvo8IhUnxRfbG8sveEY91+T0CfcFqeUaVGKhJ10fweQ7uRzXwARDDtifEZjOwvxxTBeAFb\nhpYkfd78OU5+SpXG58DWmbcjNZ87XstV6flw9913S6pUnajDIPdRPoBYYZHqSWQ9+vzbKqdK48+z\nIIv3E3V8pHLhz9f1GTHsc8DCLR6DOxIyqIwP9Kh/CWs7LFy4UJJ01FFHaaCUsTlSdS2toFn1YOyC\nz6MVOSoCvH7MhqBKQom5rjKjt0UZmrVMqGTxj9ZwdkxNknbp6IVCkiRJUo/TqX//939fUnNDLlYa\nZPVDuo9KV4wXNqyQyEBev7arKESl8aMsB78y1Zhluu1WKg0OugJN1LCLY4pSuaNy9f65F9s0gmgg\nUtnw9+q2HxVIo/FMt5uNklWloxcKvHA8oZxEpYVOf64nNAtWsKCI91VOOKm5/0LUs56Tq7z5fHFp\nmbSqwmZLqVWlRvpc2XMgSgMqj4sWj4+bDwhGbfs8slOiP+fAKV8X/5x1/Eto9ZvBdEi76667Gr5L\nv38kN9bFoLDugWHnQxZF8fGwyyQfuqz0yG6i3h595FHp2RIej3uRuFooC7hQlWI8RhRHZPw9Vp+0\n731VOOiggyRJ1157rSRpzz33lFTFLnhslq99rR3o5evjc0HFLepvwRoIvl5189K/o+wf9X4w3vdQ\nViFNksHS0QuFJEmSpB430ooamjHjIGpnzqJP5bZYGZGtmmmsccFjohRtboe9JaImdd6PlQ8XoStd\nhjScojoGUU0CBpq2CqJndoRfadhG5yJyP0nNi9To1bA0/arSEQuFoS4kUvfZqGDF6i4kIsUy29/8\nzd80fC4qJuJmJytWrOhTSCZMmDDgQiLlNld3MZGokEj5/1bFRFh8pT/YVCaKRYioOz+R6hDFr0Rx\nM/x8f/VBorGUDCSGwxYum8lEmUamlaoT/Z7X3YpG9AxoB98DrMkRVVvl/dfq+nDsq9LPpO458dnP\nfrZvLnPOl+V8k2Sk6IiFQpIkSTIwmO3gBZANDLoJuZDlIrCuEBv7FTAVlwG8HlPkijVR4bqoPHlU\nTZLHXKoIrYq9Ee6z3fMVqR10z9G16EVy1Kqg/FlkoPF6mP56XgyGjlgoRIVErr/+eklVZTafUNY8\n8OQoa9t7JW6pzMVE/J5+4KjuvmvPex/cN+UkFvGos068DU8UR2P/5je/kVRd/B133FGStN1220mq\nFJbHH3+87/1Xv/pVrbXWWlq0aJEmTZokqfIjRz0yyjExhSdSALwt+t9bFRdxbIKj5y2Xen/8fLkt\nBmH5vHm+HHHEEWoXR8gbnysH+9RlCpSUdR4YDMWgJebCc9t86PrV5zby6xs+xDlmH1v50IxUjl//\n+teSqrnn7zhjwPExkZUeKUdRpg5jU774xS9KGlxZWWJFzr1S/vAP/1BSdQ/zj5XldBfx8TliJgyD\nANkx0+/LOBu2DmZV0Si2I+oR4Rops2fPbutcJMlQ0hELhSRJkmRgRKXO6SNnHn87vR+YsuvFrhdA\nrKtA93Dk748Wt/x5ZNW3+n3dNiOjLiqo1WrfUTyAF5asdcC6FV4kuxAYU/LLOA+6/FggsK7EfPk5\nulwHS0cvFJiZwJMSVaqTmvO3fZH83tBComzkixddEOaL+/ssBFJ+1vtkV0f7Wl2jgCkx3ocn4BZb\nbKGxY8dq7Nix2mKLLfqsl8ifXNd/wROKFRopbdFiZBaIz4vHwHKnrFDXTnyJH1J88FmFWBVaVdiM\nJL7yGJgx42OnkhDFy1Bm9ZyjysKqlVQiiAO7rCxIzQ83b9tzjlkNnHukVd+VqPaF33OuXnTRRZKk\n0047rXZ/JXPnzpVUnUcXk/nWt74lSfre974nqerOatWIPR78QLfKZdWR59X7ocLF+7m85xlgyM6g\nkZLgucXzlvUUkpGkoxcKSZIkST10+zFK33CRyCBqKg9StZD0Asf1C2hA0Qrmophjol/fRC4/Q1Ug\nUkfK7URxEFG8A/fVqoFaFMBOly9TYw3Tb025UKUxwZRcxihEqburSkcvFOyPczMPWyEsLMLVePkz\n5qj7lXXh6YuPqu2RVr0LysnEiWUrwbEV9qWyRgPlprLC35gxY9TT06P11lsvrGRJq7guwyA6nqjM\nKS2dqO49x24rnGMqt+9rZN8/lYXBlCNtFdnOBxCt9vIhyloTnktUElgFlI1x6PuOqmAyI8HvWfPA\n+/WDqVTPrF75gfPCCy9IitWrVkQPyXZrXDAGxuPrDysJzASg/97H6GPz+afK5337j6A/H8ng/J5h\nbEO5Lwa0UWEzvA94r/ZXAyVJhpuOXigkSZIk9TAtlCXPW5XAZ8XBqAZC3XeoKLTaFxfeUcv0iKi+\nAq3+0uCgcROlbptosdsqViGKcWABNi9Yfd14THXHEB131MHS0E22quQyNUmSJEmSkK5QFCzBWnq2\nVBhV0Cp/FtUkt9TKqF2+b+Vb4/6433KlzL7lLJNsFwSjYb0CZcOecePGNVgFkSRKWbNczdflUJew\nZXJUlYxpcT4mpqM6hSxqW11+lj6/uoplAyWqBMfx0G9bXscofZHysl0APraoAFAkNzOVMIp49n4t\nc7M8syQ99dRTDfviOWajo7oGYuW26e9tN9+dgYL+vAMK++t2RyuJqYaXXXZZw+9ZZ4CwNHa78yya\nQ2UsAK81S5vTvREFpkY/L7fl5yN941HAaTvF4fxZW8HMfuCcjjIBoiwH026htyhugM+20k0XXceo\nYFbk3mlVuI7uabpZ/ep+FL5unp+0+stxM7jVc5XXlHM4UhoGSyoKSZIkSZKEdIWi4OAlKwlexbJ1\na9lAhRaSV/RexTGNkXnB9Bex3jct6qhdbF3gIK01+v0cHOex2yr3cbqIUemTHDt2bJN1GEUIl2Ni\nQChX4a2CG73ijYKvfI0cKMYAPqZflsfv8+TjshpxySWXSJJOOeUUtYu3QSuMVhbPVV21NFvuXN0z\nj53569wW21F7jvp6+5Upvj5nZ5xxRsMxXnHFFQ3bL61R7+PMM89s+M4FF1wgqVkxs7rlY6Ul18pK\npbUWVbcz3p/H+aMf/ahpW5z3PJ+eN77Ht9pqq4ZjiHzTHCutZN4jkd+4rn07ryHz2vn8oDrFFEyp\nmv+TJ09uOA/G84SpyITnsa4DpOeu718qJVGcQ6RaRP0mTKsS8Xz+sI9CqVz4foq+G6lgUbxFqzlP\nJcH7f/311xv252JmrGJZ166cf5eirJKoyZ0/v2DBAknSrFmzNBhSUUiSJEmSJKQrFAWnTtmiYEEe\nr9rLQjxe7Xsl7/dRfAMtHKaeRRXAuFImdf6/VitZj9Fjfv755yVV1rgVFrY+jnzgUX513WeoQkTd\n3piny7RHNhtynIWPzYV9WOmtPL6zzvr3hm16n7NmDb71LiuW2ZKldUUrpK7+OlP8opRKWl0+hyx7\n7XNk5YR97Fv5s0888cQ2zkAjX/va1yRVsQG+Lr5OPB9RmizvE8LzyPvF59+xCj43UnV/8xqx1LIL\nK3kbU6ZMkdSs9hn2CWAnREb0Uyni9a3zB1NxY/toWuLR/CvxtfFz0d+JWoRz3vCV8Vrldz0+z0n7\n2TkuHkerxnRRYzyqNFFsgu9fW+tMIZaqWC/DuRvFW0WxHVH5cm+XSqDni59nTO/3dv/X//o//3Ps\nK6/f2WdP6xsj54mhEsXXqMDZYElFIUmSJEmSkK5QFLxK+t//+/+VVK3ELrro/5JUrdBK3w6jdf1K\ny5hRyPShM+o08r1FFlddS2eWhDW0aGztummULSX7tkrLsre3t8n6Z3Q84yvqxhsVYOI2Df1q/jyj\nvD02WkM+32V8yWmn/d8N4201xnY46KCDJFXFu6JoYVpydX5LWk1RsS1a0ZGSYP8vs3v8fTYWGorO\ncI5ncMO1P/iDP2jYR6vCS/3lr5fbifK+I4vUiob971KltNDSp2+djcccmxCN2UqCt89ia1QumQ/v\nY/LcLffHQku0SluV+fYcqLvvfI5YBjuqC8AYGaocprwHv/GN/6/hdxdcsEfDOWCtAGYoeYxRDBDv\nlah3QfS89j3ja1dX+ttlwfksj86ToZrKmJWoRoTPhcfIxmBUnE488cH/ObZGxbC0/lm6m+eT157K\ngj/He2GgpKKQJEmSJElIVygKUSMhW6VeLdXl4nul5ZK19rVF7XD5c+878k/RouqvARMtmug4I/WC\nq8QPP/xQvb296u3t1fLly8N83iiat24sraKMmTMeRS0zytvXymqIr5kVhnLlTGWEYzn++OObjqNd\n2PKZlq6Jslqk5toTPN8s380IeCoJVl+8DzYLs7LEKOk777xTUqU4DeS8fOxjH5NUNY6i6sE5yDzu\n6Lpzrtn6ritpXm6H7dlLC5pWFK0mnxc2tKL1yhoSnptUEqI6GGwvzXoNZVvvqFqfYU2TKH6CYy73\nE6k/kSpmuC9fo1JR8Ff82bPPXqkwXHzxJyU11/jwa1njpdx31GQtUmCjjALfQ46V6K+JmH937rnn\nNnw38ufzWkTqMc+fv0eFh7EfPnYqUr7WP/jBHzR8r24MkdLJvhF8Rq1qU7FUFJIkSZIkCekKRcEr\n+fPPX+kn8+rWq1Kv5Eo/jH1XXon6d8xptbXGVaS36ZUyK/dFdb7587pMA1qizN33cdHyZP5+uUov\no35p7UXRvCVRLXGuehnd3G6deI89Wm2XFuQll3xKkjRnzs8b9v1Xf/WxfvfRDsw8oFrVqle9VFkE\nvH5RxDbzqf3KGARbxLZU/T22LDaDaRR07bXXSpJ23XXXhuOjL5g+T0aAm0gdY30MxoBEnf3omy+3\nwfnLOcjsk2gfxuc5so5Zj8DfZ70TW/jluWBjLr+nYsK4C1bp8z5LtSiqf0Drk6oozwtjMPzMlKRz\nz/19SdJf/dV/NWzr9NP/H0nSlVfu1zAWHx/jdtq1zhnDYHhvUd1pBx+v77sofoSvUQZBpLI5Niaq\nsOq5YGXjO9/ZTVL1LPS1LxWiKPslGkOUHdFus7eIVBSSJEmSJAnpCkXBcQVe+dqX45WZV1te4UvV\nqpE52La8vQK2P5gd2GjxejVIa5A+d1ayKy39KFI8WoEyY4OWTmnFr1ixoska5Gqd2Q9Sc8aIj8/n\nzcdja5dqBv163Cfb7TJC3NZO2RLZ2/7hD/eSVFkPL774olYVr7h9XXxuPXd4PGwVLTX7V3ndWKHN\nipDzur0Pxwdw37yOtBw5X3xOb7jhBknSYYcd1nTc/p2zHFr1NWjXmqICxfcmiiw3tNr7U0toQXMO\n0XdPVYg1QHhf0fL2djxnWTmU/vNym8ymYiwGe89EVT5LeFysEEor0vuiQkKrvtyXnwNVZoS32egT\np6rCeUMYZ8K6K9G59zFw7g8EXx/fl2VMSTkGv/qaRf1seC8wPoDzjJl3VBDrsipadbiMlD7GBNXV\n8xkIqSgkSZIkSRLSFYpCVJ+ffvQS+n3o3zeMGo38VoyItnXIjo5R/m8drJjG7zCTIqrD0NvbqxUr\nVjRlaBj6hUuVw/+njziK/6AfuFXHSuZ82wLxMbGKWd04me++KvzJn/yJJOnuu+9u2If3z/lRl6kS\nRXDbYrACZquM/ecZLc9z6+2w/n/UqdHj8OfcRbG8L/bYY2V8z8SJExt+R+uS1nr0yjkVVb2Lugfy\n8zzPpQUU3ZPslcEumuzvwd4bdf1YpOY4Gm/P18v7973vOVyqTqyySkWNkfeGVn+pUpAojsSU2VH9\nfZ9WezmOr31tZb+M733v2YbPHHPMfZKka675k4bjihQ53ivsh+Brw3nImAfP8R133FGS9Mtf/lJS\nc0aQVF0Pf4fZRN4345SoDHosvpauEOxnWRTXxPPKugqMKxiIosD3jLniNe/v71A7pKKQJEmSJElI\nVygKXskZdlOsW01yFWer1PXg/T7qhhetgL0Pdni0FclKa3W+oVa51fx5VNvAdRR6enq0bNmy2nrn\ndd8rx8TVMGM0WvWJiLq5eXXsSGBaVj7/7Izo4yq3ZctgVf1sJa7UeM899zTsizX366xOrt79XSte\nngs+DlsyVGNYKY41HgwzSugj9XWnJexxSJW64eqF0ZyIlAKqVFGmR1RFM+q4SF881QGpuTZ+lDVE\nvza/x3s7sqgZn+Rzw4h7vzKDQarme1Q9kf5u1nbweXC9gDqiXHoT7Ys+9DplllZudR/of45VDcdO\nfz7nl5/ZixcvllTdK7ynmVnCarQ77bSTpOq81nW8JL6uH//4xyU1V1B89dVXJVX3CDPljGOkpk6d\nKknaZpttJDWrpHymeuyeV63+5vSnXkbKW7Rv3n+DJRWFJEmSJElCukJRYO12+oS9Wi39OV69sUuh\nrTv6K00USe19sP8ArcioTnj5M64SbYlEHfui/OfSP/Xuu+/2rYg9Jka+1vlFvXL3vv1KK8pjYb9z\nQxXA59vn3/Uq2GmNykK5LZ8XZlwMJax1YJjPXs4TWyKcG1S4fO2ZZ+/rwyhr5tQb1n7we4+Z9dx9\nvkr/tjuQ2r9qiyyKmo7iZTj/o74Tvob03zNTw9ha8xzeYost+n7nOUm/ddQ3ghYbFckoJ50R+Iyf\nibIGmA0hVdfQx+254fuH8Q78nPd93HHHiURqEH3cVMVa9VkorU5a9s5A+sY3fiWpqnVCBZVj8z3y\nxBNPSKrmITNTqBputdXK2AhW3x1Il15eZ2+D/Xx83C+99JKk6v70mLxPH4vj07z9nXfeueFccP/M\n8KA6EMXQlJ+J6ilENR/4+brsmYGQikKSJEmSJCEdrSi4w51XbLZCospidd0jvcr3qy1/1iKndcJ6\nC/SDehVO3yJX5+WYuA1bFdOmTZNUWYK0bBgl67Guu+666unpUU9Pj8aPH99nhbBHu79vq760zGkR\nME85quYY1ZmndWWLwCtej40d+UprxteZlrnHcMkll0iSTjnlFK0qtGjoI6/ruElFwb/zsXr8rHfB\naH3DyGUqJzx+Rmkzyp/ZFeXvousaxX9wLjJ+gtarj9mWo33Snos+N1Y2WAnVVp1rT0jNPR2i/iyG\n9ShoTVEliSxrWnM+Vmar1PnL2euE22A8EWM0+otNiDKqoliFKMaJGUrlPRjFL8yf/9mGY2dsAiss\nusfOf//3f0uSTj/9dEnVPcx4CcfQWFEoOyn2R9TXoiR6ljkTyM/6xx57TFJzFV6frzlz5kiSrrnm\nGklVzILj36LMBN6f7P1Sd49F9SKi+BqqGr4OpWI7GFJRSJIkSZIkpKMVhajyVZTHXq6IuUJnHjN9\ntLQcoxxrWgb0n3O/Xi1KlZpx5plnSqr6pdPva6vbx21L1cf/yiuv9H1vxYoV6unp0Wuvvdb3c+YP\ne4zMPCi3Sf8mV+hRDwh/n9XFGPXNWASPkeex3BZ99ZE/fFXg9aU1WWcB+hhoBdEXzm0wGt/ngKpO\nlGPPsXk7rDrq35fXecKECZKaM4SibJYo24UxDbwnrSA888wzklqrPrbOpkyZIqmaV1YWpOqeYlwD\naz5wTK16HURZRlSA2M+E1jT3U461ri9L+Url0c+IuuqahhHsrfzXVBiijoTleeC5YSXWqL6G8Vz0\nfLCSYDgmK5BWj61+1vVaqaNOSeDPopgWXyNXLfXvHVfBzxnXR7H65THzbwnnF3v5+LWuHlDUt4Rz\nk/FNzKbx35TBkopCkiRJkiQhHa0o0I9H3yR9umVlNP/f1p+tAK/66OeNuttF/RJYd4DWImu6S80r\nRvZSd0zGiSeeKEm6/PLLJVVZH17Bzpo1q+873s9+++0nMnfuXEmVtc7aCO3QaiXv47PqYWxd+5jZ\nr4O9Mkr1wNukpextDUVsgnGlxoibbrpJUqOPz+PgKp5594Srf/uvfXy0WKLobFq4zEDwHC0zB6wo\nMN7BtOq0SEuS3/f1tXXVTn67VM3fp59+WpK09dZbS2qsw+9ts1Ieuyiy0h0VGt67rXLRozoEhqpA\nea/wPEeZFYx5KmMzkqRT6OiFQpIkSdJI5EJhAa9o4RMt9sqFUFQEKEqD5M+Z5klo9O2yyy6SqsVt\ntJgzkYu4nWZRTF33d7ww33777Rs+99RTTzXs68ILL2x478BTB2ByUc39msg1VC7SI5c4i715WywR\n7kX2ySefXHsu2qWjFwq2HB944AFJzXUH+osn8MlmFS4rCozSji4ilYbIr8mbj/EBUuvIUysJxv7b\na6+9VlKjktAOjMuIJrAU+8Ai/y/9wMxz9ufZhc7b85hYT19qtqTrqjeuLhiRLFVxF7Z6HS3NKHhG\n31O9Yv8QZitEXev8e+ZIe4ysiSHFFn5U6Y0P8uihx4clK5S2y8EHHyxJuu666yRVvmqpypCwosYe\nD5FCFtUZaNV5j/EZ3o735/0zJqS/aHWeT5839rHpLzYhSUaKjl4oJEmSJI2wXTFdH+2W9+Xir7Rs\no0ZcURAsf+4FY2Qc+XMOIHQwaxRM7YWVF2c2QLxArQtop2vOhZJoQEVl7LfbbruGnz/++OOSqmB0\nc8cddzTsmy7IqFmUjUif67qCS1SPeJwM1OXnWUhusHTFQoG+XFtsrIVQWk2snsbKcra2eIL5GuXA\nRtUSo4hkqbl2eLvMnDlzUN/jTV6XMx9ZY8w0oT+efvKoWp4/72vlm5N19csYBZ9TWvOrmgs8EK6+\n+mpJzX5wqVnmM47Udz416ypwzng+RL5zWqp80LCaHWNRSpitYxiLE1Xe5Pc5Vu/bYzr++OObxtAO\nRxxxhCRp3rx5fT+zusBYHc8HdgDlHIui3U0ka/N68aHMOKf+tsmxMMPpwAMPrB1bknQCXbFQSJIk\nSVbCBTRbNHPh0m6TuXJx48WWDaooUDcqtGRLnwHbZtKkSZIqRYGuMQZ9WqGwhez3S5YskVQtEktV\nxC4rwwWejzGy/H3MLojnMbHgmxfHdUGtde8N26J7f6WiQFd3pCjQ/U535qrSFQsFWxC21Hxj+AT7\n52WkNDMm/NnIuo78lgw44c3JC8mOfuWFsjIyUH70ox9Jas5FboUDiXyz1/WdjwKaqBhQSYii4Bl8\nYyWBcQf8fl0NDPreV0dE+IIFCyRVDxF2fpSqh5THx8/YSvQ58AOLDyb6q6nORD52xjL41XOQvnSp\nuXCPhK4AABloSURBVN4HFQLuO4pdMFQ7PIY6NWMwzJ49u+//rjfiehU+D34usPOnr4czPXzsUWfX\nqDMfA8dYQ6S/wDneF3zQ+w/pjBkz+jsNSdIRdMVCIUmSJFnJ0UcfLUn6+c9/LimOmo+aQUUZAnWK\nAot/tUqt9kLIizjihfjee+/dsB8TLdrYQp1uNxYvqxsr1REeU6TIeBFs9cOfu/LKKyVVhkBUvIzH\n5oWn1REalXVtpnksdA3T6Biq0s2mKxYKhx9+uCTpwQcflFSdWHbrK+uCR70cWqWoRL+PlAh2AKQ1\nUtZ28Gfs/7YlxHoQntC2iPbcc09J0r/+679KqizaV199Ve+88456e3sbfLqs5WDLl6pAedxUEjwW\nVphrtzcAlRbWkIjyyMvz4nH7hnIWyHDi6xVZo1JztoOP2T50E6lUvi58KEZzNXo4s5eBJWkrL2Xc\nB7uC+nrwD0HUR4D7NFHsQitcI8QPsrvuuktSdf5fffXVpn36OTBQ/EC38ujz71dmQ3AO+znDGIX+\n/vDy2vq7Rx111KCOIUlGkq5YKCRJkiSN2GqnosC2xSYKQGaQbPl/LhwjV1RUR8HYr7/XXivbVbfb\nQIlB0mVJ/PKY6oKOoxRdL/CZpszzyAW6F96OWfA+fW7qGsiVRE25GIxbumG5GI1c5VHjuGOPPbZ2\nLAOlqxYKvjFY9ZB9v6U4kpm56FHtcp74KOeaVh3lo9JSZkyFP+NYAu/TE5H17z1mKwqPPfaYxowZ\no97eXo0fP74p3caWZX89zctOlFKzghBV8jM8z8xQ8DXzeYm2V46J3UBffvnl2n0PB1Yt7r33Xkn1\n42Wt/0geZWc8Fp9hvXbOSePvW2nx9XcshLfL2AkXgCn3QSLZtdXDjted1neE1TTGDO2www6Sqnu6\n7J7ozoOD5YQTTuj39/Pnz5dU9RvwmKhotdpOkoxWumqhkCRJkqyEhhMDqY1dZUwbjQKXy/+zrLiJ\n6h341YteKwlOcd1yyy0bvk/LOGqWVdcwqTwGLvLKsXsfXmj7vPkYbbhRIYhShn1skydPbhgrjc6o\naqXPN92adLlKzW72qGkdDYEoRmSwdNVCwZ3IXF2RvsXSyqFkxovJ6nqMiKZSwIkapaFwopc3rSci\nszdssfiis6a/x0hZj90UfWz2lftznjR10dpRVzhmdXjsVBro82YXPL/y/Ed+4XIbtpiPO+44rW7o\ney8fQFGxGVrj0U3OB0jUadAPjueff16S9OKLL0qqzqm3+9WvfrX2GP75n/+57/8+35FSQNWJRClk\n/rzncCR12mqntW48f3wu/OCXpCOPPLJ2m0PFSMyvJOkmumqhkCRJkqzEC0bXJLClG5VXZzotF/11\n7ikGIzP4mQtHb8OprDbInDFAd7FhCirrJxgqHWywVtdczngx60Wqx8DCXVzQ21Bji3ZmHETuVBpT\nVBa8KPYiuTQu6YatS7sv8efLNu1DQVctFE466SRJ0i233CKpeWKXF4qWPjvPGU4Glv108Iz9wOx1\nQOve3/OkLK1PT2oWDfF3bfnbJ28FhVXvbFm+//77+vDDDzVmzBgtW7asb6wM8PF7Fhopzw8nJGMu\nHDXPyP6oSUnUg93ni/UYSsXG5+WQQw7RSBFV9pSaS7VG+fc8NywK44cac+0Z+e/rSumzFeV1plLG\ngCrTqgwwt+PXqN/9xRdfLKlSAX0sLlZz9tlnS5KeeOIJSdVcL2tmuGFQkiQjQ1ctFJIkSZKVuCOg\nA29pxLDsOmsjGLpZpeYUUFrLLB5mvAh2F0iWKzd0r9mq56KZhocVCh4bMxnKsRimtxp/l8XfIrc1\ng5VNlBpMt6zHEQV219WC8PFGyoLf28hig8FVpSsXCq5mduedd0qqrNzyBqA1x1x1RmlHUf1RHAEj\n3r09RqCXvlYTRatbMRkIF1xwgaSVNfLdZZKTy2NlBTsptjQ9IXnjGn6eyo1/T+WACoYpH1L2yY8k\nVndc5a88Z0zFYvGXSGUxDCoznmN8oHF+++e2zl2TwBkbV111lSRp9913D48veqgx6ydSEpjV44Y7\nhFUjrSQYKwhf+tKXwrEmSTKydOVCIUmSJFmJF7Us1c5Feqt22WVwNAvIMdibBdiM3zMA29AQcRos\njRi6JNnPgov1uoJ5LPrm47Z6wTLzNGYY6O0xRGpKVNnR42D6cquU/PL/zA6hi3y4U8m7eqEwffp0\nSdJtt90mqbJOpOZ0G6YA8aJSDooCVRiLYBipb+pUDt8Ufh2MklCH980gJUpkdf5kRtzzAcE0Kd5M\n9Fn7WBn4w4wAn4NXXnmlb9unnnpqG0c7vLhj5z333COpqtQoNVcxZIYEpdxINvU54fXgnGVBGP7e\n5/KKK66QVLXHLeXeqBSsibIZDBU54z9S7vpIPEY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"text": [ "" ] } ], "prompt_number": 175 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And here is the problem. The outliers are clearly gone, and it's because the distribution has been truncated:\n", "\n", "![img](http://www.vbmis.com/bmi/share/chris/t_to_z/truncated_t_to_z.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Properly Converting T to Z\n", "\n", "I found [this paper](http://www.stats.uwo.ca/faculty/aim/2010/JSSSnipets/V23N1.pdf), which summarizes the problem:\n", "\n", "![img](http://www.vbmis.com/bmi/share/chris/t_to_z/correct_t_to_z.png)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Implementing the Correct Transformation from T to Z\n", "\n", "This was modified from the code provided in the paper above. Thank you!" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = all_copes.get_data()\n", "\n", "# Let's select just the nonzero voxels\n", "nonzero = data[data!=0]\n", "\n", "# We will store our results here\n", "Z = np.zeros(len(nonzero))\n", "\n", "# Select values less than or == 0, and greater than zero\n", "c = np.zeros(len(nonzero))\n", "k1 = (nonzero <= c)\n", "k2 = (nonzero > c)\n", "\n", "# Subset the data into two sets\n", "t1 = nonzero[k1]\n", "t2 = nonzero[k2]\n", "\n", "# Calculate p values for <=0\n", "p_values_t1 = t.cdf(t1, df = dof)\n", "z_values_t1 = norm.ppf(p_values_t1)\n", "\n", "# Calculate p values for > 0\n", "p_values_t2 = t.cdf(-t2, df = dof)\n", "z_values_t2 = -norm.ppf(p_values_t2)\n", "Z[k1] = z_values_t1\n", "Z[k2] = z_values_t2\n", "sns.distplot(Z, label=\"Z-Values from T-Stat Data\")\n", "plt.legend()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 173, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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DH/ZbUEtL8vZ4cnMdUn+c+AJh1jz/AYqiUZxnwOcL4ev+e4/f4gugqtHYuuFe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\nQmCC\n", "text": [ "" ] } ], "prompt_number": 173 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Viewing the new Z Score Map\n", "\n", "Did we fix it?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "empty_nii = np.zeros(all_copes.shape)\n", "empty_nii[all_copes.get_data()!=0] = Z\n", "Z_nii_fixed = nib.nifti1.Nifti1Image(empty_nii,affine=all_copes.get_affine(),header=all_copes.get_header())\n", "nib.save(Z_nii_fixed,\"/home/vanessa/Desktop/Zfixed.nii\")\n", "plot_stat_map(Z_nii_fixed)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 176, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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kaSlHv5aXWtqUCl1mLMR2Rnb+iKUtVD0QlGXJMT0a0ZTQ63QFPc7hAqDB2GXA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\nTlju4XOh+QpWxr6lamlZPe9u2T8MSljuJFNSvAjsV6evSrf3WqrBMNV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