├── 00-introduction └── 00-fMRIDA_slides.pdf ├── 01-reproducible_neuroimaging ├── 01-fMRIDA_slides.pdf └── Git_Jupyter-cheetsheet.pdf ├── 02-fmri_data_manipulation_in_python ├── 00-structural_mri_manipulation_template.ipynb ├── 01-functional_mri_manipulation_template.ipynb ├── 02-data_frames_manipulation_template.ipynb ├── 02-fMRIDA_slides.pdf ├── bonus-data_structures_and_oop.ipynb ├── data │ ├── CSSE_confirmed_2020-03-26.csv │ ├── covid-ecdpc.csv │ ├── sub-01_T1w_space-MNI152NLin2009cAsym_preproc.nii.gz │ ├── sub-01_ses-1_task-rest_bold_confounds.tsv │ └── sub-01_ses-1_task-rest_bold_space-MNI152NLin2009cAsym_preproc.nii.gz └── images │ ├── brain_images.png │ ├── fmri.png │ ├── mri.png │ └── random_picture.jpg ├── 03-fmri_data_preprocessing ├── 03-fMRIDA_slides.pdf ├── data │ ├── harry_potter_400x400_array.npy │ └── sub-01_T1w_space-MNI152NLin2009cAsym_preproc.nii.gz ├── fmri_preprocessing_nipype.ipynb ├── images │ ├── harry_potter_400x400.jpg │ └── screenshot_solution.png ├── linear_transformations.ipynb └── pyscript_realign.m ├── 04-general_linear_model ├── 04-fMRIDA_slides.pdf ├── 05-fMRIDA_slides.pdf ├── glm_on_fMRI_data_template.ipynb ├── multiple_linear_regression_template.ipynb └── simple_linear_regression_template.ipynb ├── 05-functional_connectivity ├── 06-fMRIDA_slides.pdf ├── brain_parcellations_template.ipynb ├── seed-seed_connectivity_template.ipynb └── seed-voxel_connectivity_template.ipynb ├── 06-machine_learning ├── 07-fMRIDA_slides.pdf └── machine_learning_fMRI_template.ipynb ├── LICENSE ├── README.md ├── data ├── CSSE_confirmed_2020-03-26.csv ├── harry.npy ├── james.npy ├── lilly.npy ├── sub-01_T1w_space-MNI152NLin2009cAsym_preproc.nii.gz ├── sub-01_ses-1_task-rest_bold_confounds.tsv ├── sub-01_ses-1_task-rest_bold_space-MNI152NLin2009cAsym_preproc.nii.gz ├── sub-01_task-rhymejudgment_desc-confounds_regressors.tsv ├── syrius.npy └── voldemort.npy ├── images └── fMRIDA_study_plan.png └── solutions ├── 00-structural_mri_manipulation_solution.ipynb ├── 01-functional_mri_manipulation_solution.ipynb ├── 02-data_frames_manipulation_solution.ipynb ├── brain_parcellations_solution.ipynb ├── glm_on_fMRI_data_solution.ipynb ├── multiple_linear_regression_solution.ipynb ├── seed-seed_connectivity_solution.ipynb ├── seed-voxel_connectivity_solution.ipynb └── simple_linear_regression_solution.ipynb /00-introduction/00-fMRIDA_slides.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fMRIAnalysisCourse/fmri-analysis-course/5495887620b54bc3f6b5c83ed9fa35df149414c7/00-introduction/00-fMRIDA_slides.pdf -------------------------------------------------------------------------------- /01-reproducible_neuroimaging/01-fMRIDA_slides.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fMRIAnalysisCourse/fmri-analysis-course/5495887620b54bc3f6b5c83ed9fa35df149414c7/01-reproducible_neuroimaging/01-fMRIDA_slides.pdf -------------------------------------------------------------------------------- /01-reproducible_neuroimaging/Git_Jupyter-cheetsheet.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fMRIAnalysisCourse/fmri-analysis-course/5495887620b54bc3f6b5c83ed9fa35df149414c7/01-reproducible_neuroimaging/Git_Jupyter-cheetsheet.pdf -------------------------------------------------------------------------------- /02-fmri_data_manipulation_in_python/00-structural_mri_manipulation_template.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Plotting structural MRI data in Python\n", 8 | "\n" 9 | ] 10 | }, 11 | { 12 | "cell_type": "markdown", 13 | "metadata": {}, 14 | "source": [ 15 | "- Loading structural MRI data\n", 16 | "- Sliceing structural MRI data\n", 17 | "- Plotting structural MRI data\n", 18 | "- Interactive plotting\n", 19 | "-----------------------------------\n", 20 | "\n", 21 | "__Packages__:\n", 22 | "- [Matplotlib](https://matplotlib.org/) - a comprehensive library for creating static, animated, and interactive visualizations in Python\n", 23 | "- [Nilearn](https://nilearn.github.io/) - a library for fast and easy statistical learning on neuroimaging data.\n", 24 | "- [Nibabel](https://nipy.org/nibabel/) - a library providing read and write access to some common medical and neuroimaging file formats, including (e.g. NIfTI1)\n", 25 | "- [Numpy](https://numpy.org/) - The fundamental package for scientific computing with Python " 26 | ] 27 | }, 28 | { 29 | "cell_type": "markdown", 30 | "metadata": {}, 31 | "source": [ 32 | "## 1. Let's load and plot a random picture " 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": 2, 38 | "metadata": {}, 39 | "outputs": [], 40 | "source": [ 41 | "# Load libraries\n", 42 | "\n", 43 | "import matplotlib.pyplot as plt # General plotting\n", 44 | "import matplotlib.image as mpimg # Loading img files" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 6, 50 | "metadata": {}, 51 | "outputs": [], 52 | "source": [ 53 | "# Loading random picture \n", 54 | "\n", 55 | "image = mpimg.imread('images/random_picture.png')\n", 56 | "\n", 57 | "# Plot image using matplotlib\n" 58 | ] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "execution_count": 7, 63 | "metadata": {}, 64 | "outputs": [], 65 | "source": [ 66 | "# Print dimensions of the image\n" 67 | ] 68 | }, 69 | { 70 | "cell_type": "code", 71 | "execution_count": 8, 72 | "metadata": {}, 73 | "outputs": [], 74 | "source": [ 75 | "# Check type of the image\n" 76 | ] 77 | }, 78 | { 79 | "cell_type": "markdown", 80 | "metadata": {}, 81 | "source": [ 82 | "**NOTE**: RGB - three channels (R - red, G - green, B - blue). Each channel includes the grayscale image of the same size as a color image, made of just one of these primary colors." 83 | ] 84 | }, 85 | { 86 | "cell_type": "code", 87 | "execution_count": 23, 88 | "metadata": {}, 89 | "outputs": [], 90 | "source": [ 91 | "# Create a new variable consinting only one channel\n", 92 | "\n", 93 | "\n", 94 | "\n", 95 | "# Change colormap of the image\n", 96 | "\n", 97 | "\n", 98 | "\n", 99 | "# Add colorbar to the image\n", 100 | "\n", 101 | "\n" 102 | ] 103 | }, 104 | { 105 | "cell_type": "markdown", 106 | "metadata": {}, 107 | "source": [ 108 | "## 2. Let's load and plot structural MRI data" 109 | ] 110 | }, 111 | { 112 | "cell_type": "markdown", 113 | "metadata": {}, 114 | "source": [ 115 | "Must have packages:\n", 116 | "\n", 117 | "- [Nibabel](https://nipy.org/nibabel/)\n", 118 | "- [Nilearn](https://nilearn.github.io/) " 119 | ] 120 | }, 121 | { 122 | "cell_type": "code", 123 | "execution_count": 2, 124 | "metadata": {}, 125 | "outputs": [], 126 | "source": [ 127 | "# Load libraries\n", 128 | "\n", 129 | "import nibabel as nib\n", 130 | "from nilearn import plotting, image\n", 131 | "import numpy as np" 132 | ] 133 | }, 134 | { 135 | "cell_type": "code", 136 | "execution_count": 39, 137 | "metadata": {}, 138 | "outputs": [], 139 | "source": [ 140 | "# Loading MRI image\n", 141 | "\n", 142 | "mri_path = 'data/sub-01_T1w_space-MNI152NLin2009cAsym_preproc.nii.gz'\n", 143 | "mri = image.load_img(mri_path)" 144 | ] 145 | }, 146 | { 147 | "cell_type": "code", 148 | "execution_count": 76, 149 | "metadata": {}, 150 | "outputs": [], 151 | "source": [ 152 | "# Print dimensions of MRI image\n", 153 | "\n", 154 | "\n", 155 | "# Print type of MRI image" 156 | ] 157 | }, 158 | { 159 | "cell_type": "code", 160 | "execution_count": 86, 161 | "metadata": {}, 162 | "outputs": [], 163 | "source": [ 164 | "# Print MRI image in Nilearn (use plotting.plot_anat)\n", 165 | "# Display plotting.plot_anat documentation (Shift + Tab)\n", 166 | "\n" 167 | ] 168 | }, 169 | { 170 | "cell_type": "code", 171 | "execution_count": 80, 172 | "metadata": {}, 173 | "outputs": [], 174 | "source": [ 175 | "# Make an interactive plotting (use plotting.view; bg_img=None)\n", 176 | "# plotting.view_img(mri, bg_img=None)\n" 177 | ] 178 | }, 179 | { 180 | "cell_type": "code", 181 | "execution_count": 50, 182 | "metadata": {}, 183 | "outputs": [], 184 | "source": [ 185 | "# Convert nibabel file to numpy array\n", 186 | "mri_array = mri.get_data()\n", 187 | "\n", 188 | "\n", 189 | "# Check and print type and dimensions\n" 190 | ] 191 | }, 192 | { 193 | "cell_type": "code", 194 | "execution_count": 67, 195 | "metadata": {}, 196 | "outputs": [], 197 | "source": [ 198 | "# Plot one one slice of the image (HINT: try to use np.rot90())\n", 199 | "\n", 200 | "\n" 201 | ] 202 | }, 203 | { 204 | "cell_type": "code", 205 | "execution_count": 16, 206 | "metadata": {}, 207 | "outputs": [], 208 | "source": [ 209 | "# Using subplots # fig, (ax1, ax2) = plt.subplots(1,2, figsize=(10, 10))\n", 210 | "\n" 211 | ] 212 | }, 213 | { 214 | "cell_type": "code", 215 | "execution_count": 14, 216 | "metadata": {}, 217 | "outputs": [], 218 | "source": [ 219 | "# Save figure with savefig\n", 220 | "\n" 221 | ] 222 | }, 223 | { 224 | "cell_type": "code", 225 | "execution_count": 11, 226 | "metadata": {}, 227 | "outputs": [], 228 | "source": [ 229 | "# Generate random data with np.random.rand()\n", 230 | "\n" 231 | ] 232 | } 233 | ], 234 | "metadata": { 235 | "kernelspec": { 236 | "display_name": "Python 3", 237 | "language": "python", 238 | "name": "python3" 239 | }, 240 | "language_info": { 241 | "codemirror_mode": { 242 | "name": "ipython", 243 | "version": 3 244 | }, 245 | "file_extension": ".py", 246 | "mimetype": "text/x-python", 247 | "name": "python", 248 | "nbconvert_exporter": "python", 249 | "pygments_lexer": "ipython3", 250 | "version": "3.8.3" 251 | } 252 | }, 253 | "nbformat": 4, 254 | "nbformat_minor": 4 255 | } 256 | -------------------------------------------------------------------------------- /02-fmri_data_manipulation_in_python/01-functional_mri_manipulation_template.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Plotting functional MRI data in Python\n", 8 | "\n", 9 | "\n" 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "- Loading functional MRI data\n", 17 | "- Sliceing functional MRI data\n", 18 | "- Plotting functional MRI data\n", 19 | "- Plotting timeseries\n", 20 | "-----------------------------------\n", 21 | "\n", 22 | "__Packages__:\n", 23 | "- [Matplotlib](https://matplotlib.org/) - a comprehensive library for creating static, animated, and interactive visualizations in Python\n", 24 | "- [Nilearn](https://matplotlib.org/) - a library for fast and easy statistical learning on neuroimaging data.\n", 25 | "- [Nibabel](https://nipy.org/nibabel/) - a library providing read and write access to some common medical and neuroimaging file formats, including (e.g. NIfTI1)\n", 26 | "- [Numpy](https://numpy.org/) - The fundamental package for scientific computing with Python " 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 1, 32 | "metadata": {}, 33 | "outputs": [], 34 | "source": [ 35 | "# Load libraries\n", 36 | "\n", 37 | "import nibabel as nib\n", 38 | "from nilearn import plotting, image\n", 39 | "import matplotlib.pyplot as plt\n", 40 | "import numpy as np" 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": 5, 46 | "metadata": {}, 47 | "outputs": [], 48 | "source": [ 49 | "# Load data\n", 50 | "\n", 51 | "fmri_path = 'data/sub-01_ses-1_task-rest_bold_space-MNI152NLin2009cAsym_preproc.nii.gz'\n", 52 | "fmri = image.load_img(fmri_path)" 53 | ] 54 | }, 55 | { 56 | "cell_type": "code", 57 | "execution_count": null, 58 | "metadata": {}, 59 | "outputs": [], 60 | "source": [ 61 | "# Print dimensions and type of data\n", 62 | "\n" 63 | ] 64 | }, 65 | { 66 | "cell_type": "code", 67 | "execution_count": 10, 68 | "metadata": {}, 69 | "outputs": [], 70 | "source": [ 71 | "# Select one volume (image.index_img) \n", 72 | "\n" 73 | ] 74 | }, 75 | { 76 | "cell_type": "code", 77 | "execution_count": 12, 78 | "metadata": {}, 79 | "outputs": [], 80 | "source": [ 81 | "# Make a plot (plotting.plot_epi)\n", 82 | "\n" 83 | ] 84 | }, 85 | { 86 | "cell_type": "code", 87 | "execution_count": null, 88 | "metadata": {}, 89 | "outputs": [], 90 | "source": [ 91 | "# Convert nibabel file to numpy array (get_data)\n", 92 | "\n", 93 | "\n", 94 | "# Check type and dimensions\n", 95 | "\n" 96 | ] 97 | }, 98 | { 99 | "cell_type": "code", 100 | "execution_count": 13, 101 | "metadata": {}, 102 | "outputs": [], 103 | "source": [ 104 | "# Plot timeseries of selected voxels\n", 105 | "\n" 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": 24, 111 | "metadata": {}, 112 | "outputs": [], 113 | "source": [ 114 | "# Generate random timeseries with the length of fMRI data (np.random.uniform (low, high, size=(,))\n", 115 | "\n" 116 | ] 117 | }, 118 | { 119 | "cell_type": "code", 120 | "execution_count": null, 121 | "metadata": {}, 122 | "outputs": [], 123 | "source": [ 124 | "# Plot random timeseries with selected voxel timeseries\n", 125 | "\n" 126 | ] 127 | } 128 | ], 129 | "metadata": { 130 | "kernelspec": { 131 | "display_name": "Python 3", 132 | "language": "python", 133 | "name": "python3" 134 | }, 135 | "language_info": { 136 | "codemirror_mode": { 137 | "name": "ipython", 138 | "version": 3 139 | }, 140 | "file_extension": ".py", 141 | "mimetype": "text/x-python", 142 | "name": "python", 143 | "nbconvert_exporter": "python", 144 | "pygments_lexer": "ipython3", 145 | "version": "3.8.3" 146 | } 147 | }, 148 | "nbformat": 4, 149 | "nbformat_minor": 4 150 | } 151 | -------------------------------------------------------------------------------- /02-fmri_data_manipulation_in_python/02-data_frames_manipulation_template.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Data frames manipulation with pandas" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "[pandas](https://pandas.pydata.org/) - fast, powerful, flexible and easy to use open source data analysis and manipulation tool,\n", 15 | "built on top of the Python programming language.\n", 16 | "\n", 17 | "----------------\n" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": 32, 23 | "metadata": {}, 24 | "outputs": [], 25 | "source": [ 26 | "## Load libraries\n", 27 | "import pandas as pd\n", 28 | "import matplotlib.pyplot as plt" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "## 1. Load & plot confounds variables \n", 36 | "\n", 37 | "**Confounds** (or *nuisance regressors*) are variables representing fluctuations with a potential non-neuronal origin. \n", 38 | "\n", 39 | "Confounding variables calculated by [fMRIPrep](https://fmriprep.readthedocs.io/en/stable/outputs.html#confounds) are stored separately for each subject, session and run in TSV files - one column for each confound variable. " 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": 57, 45 | "metadata": {}, 46 | "outputs": [], 47 | "source": [ 48 | "# Load data\n", 49 | "\n", 50 | "confounds_path = \"data/sub-01_ses-1_task-rest_bold_confounds.tsv\"\n", 51 | "\n", 52 | "\n", 53 | "# Print first 5 rows of data\n" 54 | ] 55 | }, 56 | { 57 | "cell_type": "code", 58 | "execution_count": 61, 59 | "metadata": {}, 60 | "outputs": [], 61 | "source": [ 62 | "# Print column names\n", 63 | "\n" 64 | ] 65 | }, 66 | { 67 | "cell_type": "code", 68 | "execution_count": 62, 69 | "metadata": {}, 70 | "outputs": [], 71 | "source": [ 72 | "# Plot mean timeseries from cerebrospinal fluid (CSF) and white matter \n", 73 | "\n" 74 | ] 75 | }, 76 | { 77 | "cell_type": "code", 78 | "execution_count": 69, 79 | "metadata": {}, 80 | "outputs": [], 81 | "source": [ 82 | "# Plot cerebrospinal fluid (CSF) and white matter timeseries on a scatterplot\n", 83 | "\n" 84 | ] 85 | }, 86 | { 87 | "cell_type": "code", 88 | "execution_count": 68, 89 | "metadata": {}, 90 | "outputs": [], 91 | "source": [ 92 | "# Plot cerebrospinal fluid (CSF) and white matter timeseries on a scatterplot (use seaborn joint plot)\n", 93 | "import seaborn as sns\n", 94 | "\n" 95 | ] 96 | }, 97 | { 98 | "cell_type": "markdown", 99 | "metadata": {}, 100 | "source": [ 101 | "## 2. Load and plot COVID-19 data\n", 102 | "\n", 103 | "Download data:\n", 104 | "[COVID-19](http://shinyapps.org/apps/corona/) (check out their GH)" 105 | ] 106 | }, 107 | { 108 | "cell_type": "code", 109 | "execution_count": 50, 110 | "metadata": {}, 111 | "outputs": [], 112 | "source": [ 113 | "# Load some COVID-19 data\n", 114 | "\n", 115 | "# covid_path = \n", 116 | "\n", 117 | "\n", 118 | "# Print first 5 rows of data\n", 119 | "\n" 120 | ] 121 | }, 122 | { 123 | "cell_type": "code", 124 | "execution_count": 22, 125 | "metadata": {}, 126 | "outputs": [], 127 | "source": [ 128 | "# Group by country & sum cases\n", 129 | "\n" 130 | ] 131 | }, 132 | { 133 | "cell_type": "code", 134 | "execution_count": 40, 135 | "metadata": {}, 136 | "outputs": [], 137 | "source": [ 138 | "# Filter dataframe by cases in Poland\n", 139 | "\n" 140 | ] 141 | }, 142 | { 143 | "cell_type": "code", 144 | "execution_count": null, 145 | "metadata": {}, 146 | "outputs": [], 147 | "source": [ 148 | "# Plot cases in Poland\n", 149 | "\n" 150 | ] 151 | }, 152 | { 153 | "cell_type": "code", 154 | "execution_count": null, 155 | "metadata": {}, 156 | "outputs": [], 157 | "source": [ 158 | "# Plot cases in other country\n", 159 | "\n" 160 | ] 161 | }, 162 | { 163 | "cell_type": "code", 164 | "execution_count": null, 165 | "metadata": {}, 166 | "outputs": [], 167 | "source": [ 168 | "# Plot cases of multiple countries on a one plot\n", 169 | "\n" 170 | ] 171 | } 172 | ], 173 | "metadata": { 174 | "kernelspec": { 175 | "display_name": "Python 3", 176 | "language": "python", 177 | "name": "python3" 178 | }, 179 | "language_info": { 180 | "codemirror_mode": { 181 | "name": "ipython", 182 | "version": 3 183 | }, 184 | "file_extension": ".py", 185 | "mimetype": "text/x-python", 186 | "name": "python", 187 | "nbconvert_exporter": "python", 188 | "pygments_lexer": "ipython3", 189 | "version": "3.8.3" 190 | } 191 | }, 192 | "nbformat": 4, 193 | "nbformat_minor": 4 194 | } 195 | -------------------------------------------------------------------------------- /02-fmri_data_manipulation_in_python/02-fMRIDA_slides.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fMRIAnalysisCourse/fmri-analysis-course/5495887620b54bc3f6b5c83ed9fa35df149414c7/02-fmri_data_manipulation_in_python/02-fMRIDA_slides.pdf -------------------------------------------------------------------------------- /02-fmri_data_manipulation_in_python/bonus-data_structures_and_oop.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Data structures" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "**Classes** are a way of grouping together related data and functions which act upon that data.\n", 15 | "\n", 16 | "A **class** is a kind of data type, just like a string, integer or list. When we create an object of that data type, we call it an instance of a class. \n", 17 | "\n", 18 | "In Python, everything is an object – everything is an instance of some class. Read [more](https://python-textbok.readthedocs.io/en/1.0/Classes.html)." 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": 123, 24 | "metadata": {}, 25 | "outputs": [], 26 | "source": [ 27 | "import numpy as np\n", 28 | "import pandas as pd" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "**List** is a data structure that's built into Python and holds a collection of items. Lists cannot directly handle math operations" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 122, 41 | "metadata": {}, 42 | "outputs": [], 43 | "source": [ 44 | "# Lists\n", 45 | "\n", 46 | "lista = [12, 13, 11]\n", 47 | "lista2d = [[1, 2, 3], [4, 5, 6]]" 48 | ] 49 | }, 50 | { 51 | "cell_type": "markdown", 52 | "metadata": {}, 53 | "source": [ 54 | "**Array** is a grid of values, all of the same type. Can store data very compactly and are more efficient for storing large amounts of data. Arrays are great for numerical operations." 55 | ] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "execution_count": 153, 60 | "metadata": {}, 61 | "outputs": [ 62 | { 63 | "data": { 64 | "text/plain": [ 65 | "(2, 3)" 66 | ] 67 | }, 68 | "execution_count": 153, 69 | "metadata": {}, 70 | "output_type": "execute_result" 71 | } 72 | ], 73 | "source": [ 74 | "# Numpy array\n", 75 | "lista_array = np.asarray(lista)\n", 76 | "lista2d_array = np.asarray(lista2d)\n", 77 | "lista2d_array.shape" 78 | ] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "execution_count": 155, 83 | "metadata": {}, 84 | "outputs": [ 85 | { 86 | "data": { 87 | "text/plain": [ 88 | "numpy.ndarray" 89 | ] 90 | }, 91 | "execution_count": 155, 92 | "metadata": {}, 93 | "output_type": "execute_result" 94 | } 95 | ], 96 | "source": [ 97 | "type(lista2d_array)" 98 | ] 99 | }, 100 | { 101 | "cell_type": "markdown", 102 | "metadata": {}, 103 | "source": [ 104 | "**Dictionary** consists of a collection of key-value pairs. Each key-value pair maps the key to its associated value." 105 | ] 106 | }, 107 | { 108 | "cell_type": "code", 109 | "execution_count": 154, 110 | "metadata": {}, 111 | "outputs": [ 112 | { 113 | "data": { 114 | "text/plain": [ 115 | "dict_keys(['wzrost', 'wiek'])" 116 | ] 117 | }, 118 | "execution_count": 154, 119 | "metadata": {}, 120 | "output_type": "execute_result" 121 | } 122 | ], 123 | "source": [ 124 | "# Dictionary\n", 125 | "slownik = {'wzrost': [134, 157, 180], 'wiek': [12, 24, 25]}\n", 126 | "slownik.keys()" 127 | ] 128 | }, 129 | { 130 | "cell_type": "markdown", 131 | "metadata": {}, 132 | "source": [ 133 | "**DataFrame** is a 2-dimensional labeled data structure with columns of potentially different types." 134 | ] 135 | }, 136 | { 137 | "cell_type": "code", 138 | "execution_count": 147, 139 | "metadata": {}, 140 | "outputs": [ 141 | { 142 | "data": { 143 | "text/html": [ 144 | "
\n", 145 | "\n", 158 | "\n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | "
wzrostwiek
013412
115724
218025
\n", 184 | "
" 185 | ], 186 | "text/plain": [ 187 | " wzrost wiek\n", 188 | "0 134 12\n", 189 | "1 157 24\n", 190 | "2 180 25" 191 | ] 192 | }, 193 | "execution_count": 147, 194 | "metadata": {}, 195 | "output_type": "execute_result" 196 | } 197 | ], 198 | "source": [ 199 | "# Dataframe\n", 200 | "df = pd.DataFrame(slownik)\n", 201 | "df" 202 | ] 203 | }, 204 | { 205 | "cell_type": "markdown", 206 | "metadata": {}, 207 | "source": [ 208 | "**Tuple** is a data structure that is an immutable, or unchangeable, ordered sequence of elements. Because tuples are immutable, their values cannot be modified." 209 | ] 210 | }, 211 | { 212 | "cell_type": "code", 213 | "execution_count": 152, 214 | "metadata": {}, 215 | "outputs": [], 216 | "source": [ 217 | "# Tuple\n", 218 | "tupla = (1,2)" 219 | ] 220 | }, 221 | { 222 | "cell_type": "markdown", 223 | "metadata": {}, 224 | "source": [ 225 | "# Functions, methods, and attributes" 226 | ] 227 | }, 228 | { 229 | "cell_type": "markdown", 230 | "metadata": {}, 231 | "source": [ 232 | "**Function** - a block of code to carry out a specific task, will contain its own scope and is called by name. A function performs an action using some set of input parameters. Not all functions are applicable to all kinds of data." 233 | ] 234 | }, 235 | { 236 | "cell_type": "code", 237 | "execution_count": 157, 238 | "metadata": {}, 239 | "outputs": [ 240 | { 241 | "data": { 242 | "text/plain": [ 243 | "7" 244 | ] 245 | }, 246 | "execution_count": 157, 247 | "metadata": {}, 248 | "output_type": "execute_result" 249 | } 250 | ], 251 | "source": [ 252 | "def suma(x, y):\n", 253 | " \"\"\"kkklklk\"\"\"\n", 254 | " return x + y\n", 255 | "\n", 256 | "suma(2, 5)" 257 | ] 258 | }, 259 | { 260 | "cell_type": "markdown", 261 | "metadata": {}, 262 | "source": [ 263 | "**Attribute** - a variable stored in a class. E.g. shape. Use `dir` to see all attributes and methods associated with a class." 264 | ] 265 | }, 266 | { 267 | "cell_type": "code", 268 | "execution_count": 165, 269 | "metadata": {}, 270 | "outputs": [ 271 | { 272 | "data": { 273 | "text/plain": [ 274 | "(2, 3)" 275 | ] 276 | }, 277 | "execution_count": 165, 278 | "metadata": {}, 279 | "output_type": "execute_result" 280 | } 281 | ], 282 | "source": [ 283 | "lista2d_array.shape" 284 | ] 285 | }, 286 | { 287 | "cell_type": "markdown", 288 | "metadata": {}, 289 | "source": [ 290 | "**Method** - is similar to a function, except it is associated with object/classes. E.c., .max(), .min(), upper()" 291 | ] 292 | }, 293 | { 294 | "cell_type": "code", 295 | "execution_count": 171, 296 | "metadata": {}, 297 | "outputs": [ 298 | { 299 | "data": { 300 | "text/plain": [ 301 | "6" 302 | ] 303 | }, 304 | "execution_count": 171, 305 | "metadata": {}, 306 | "output_type": "execute_result" 307 | } 308 | ], 309 | "source": [ 310 | "lista2d_array.max()" 311 | ] 312 | }, 313 | { 314 | "cell_type": "code", 315 | "execution_count": 175, 316 | "metadata": {}, 317 | "outputs": [ 318 | { 319 | "data": { 320 | "text/plain": [ 321 | "'COS'" 322 | ] 323 | }, 324 | "execution_count": 175, 325 | "metadata": {}, 326 | "output_type": "execute_result" 327 | } 328 | ], 329 | "source": [ 330 | "slowo = 'cos'\n", 331 | "slowo.upper()" 332 | ] 333 | }, 334 | { 335 | "cell_type": "markdown", 336 | "metadata": {}, 337 | "source": [ 338 | "Attribute = characteristics; method = action" 339 | ] 340 | }, 341 | { 342 | "cell_type": "markdown", 343 | "metadata": {}, 344 | "source": [ 345 | "## Building a class with attributes and methods\n", 346 | "\n", 347 | "All classes have a function called `__init__()`, which is always executed when the class is being initiated.\n", 348 | "Use the `__init__()` function to assign values to object properties, or other operations that are necessary to do when the object is being created:" 349 | ] 350 | }, 351 | { 352 | "cell_type": "code", 353 | "execution_count": 188, 354 | "metadata": {}, 355 | "outputs": [], 356 | "source": [ 357 | "class Person: \n", 358 | " def __init__(self, name):\n", 359 | " self.name = name\n", 360 | " def hello(self):\n", 361 | " print(f'Hello {self.name}!')\n", 362 | " def goodbye(self):\n", 363 | " print(f'Goodbye {self.name}!')" 364 | ] 365 | }, 366 | { 367 | "cell_type": "code", 368 | "execution_count": 187, 369 | "metadata": {}, 370 | "outputs": [ 371 | { 372 | "name": "stdout", 373 | "output_type": "stream", 374 | "text": [ 375 | "Goodbye Jarek!\n" 376 | ] 377 | } 378 | ], 379 | "source": [ 380 | "p = Person('Jarek')\n", 381 | "p.goodbye()" 382 | ] 383 | }, 384 | { 385 | "cell_type": "markdown", 386 | "metadata": {}, 387 | "source": [ 388 | "## Libraries and modules" 389 | ] 390 | }, 391 | { 392 | "cell_type": "markdown", 393 | "metadata": {}, 394 | "source": [ 395 | "**Module** is a piece of software that has a specific functionality (contained in separate `.py` file). For example `plotting` module from `nilearn`.\n", 396 | "\n", 397 | "**Library** is a collection of modules, e.g., `nilearn`, `nibabel`, `numpy` etc." 398 | ] 399 | } 400 | ], 401 | "metadata": { 402 | "kernelspec": { 403 | "display_name": "Python 3", 404 | "language": "python", 405 | "name": "python3" 406 | }, 407 | "language_info": { 408 | "codemirror_mode": { 409 | "name": "ipython", 410 | "version": 3 411 | }, 412 | "file_extension": ".py", 413 | "mimetype": "text/x-python", 414 | "name": "python", 415 | "nbconvert_exporter": "python", 416 | "pygments_lexer": "ipython3", 417 | "version": "3.8.3" 418 | } 419 | }, 420 | "nbformat": 4, 421 | "nbformat_minor": 4 422 | } 423 | -------------------------------------------------------------------------------- /02-fmri_data_manipulation_in_python/data/sub-01_T1w_space-MNI152NLin2009cAsym_preproc.nii.gz: -------------------------------------------------------------------------------- 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{}, 6 | "source": [ 7 | "# fMRI preprocessing exercises in Nipype\n" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "All interfaces: https://nipype.readthedocs.io/en/latest/interfaces.html" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 2, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "import nipype.interfaces.fsl as fsl\n", 24 | "import nipype.interfaces.spm as spm\n", 25 | "import numpy as np\n", 26 | "import pandas as pd\n", 27 | "import matplotlib.pyplot as plt\n", 28 | "\n", 29 | "fmri = '/media/finc/Elements/ds000003-00001/sub-01/func/sub-01_task-rhymejudgment_bold.nii'" 30 | ] 31 | }, 32 | { 33 | "cell_type": "markdown", 34 | "metadata": {}, 35 | "source": [ 36 | "## Realign" 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": 3, 42 | "metadata": {}, 43 | "outputs": [ 44 | { 45 | "data": { 46 | "text/plain": [ 47 | "" 48 | ] 49 | }, 50 | "execution_count": 3, 51 | "metadata": {}, 52 | "output_type": "execute_result" 53 | } 54 | ], 55 | "source": [ 56 | "realign = spm.Realign()\n", 57 | "realign.inputs.in_files = fmri \n", 58 | "realign.inputs.register_to_mean = True\n", 59 | "realign.run() " 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": 7, 65 | "metadata": {}, 66 | "outputs": [ 67 | { 68 | "name": "stdout", 69 | "output_type": "stream", 70 | "text": [ 71 | " 0 1 2 3 4 5\n", 72 | "0 0.000000 0.000000 0.000000 0.000000 0.000000 -1.880791e-37\n", 73 | "1 0.001749 0.031678 0.074810 -0.000678 -0.000291 -5.601287e-04\n", 74 | "2 0.028545 0.036790 0.152968 -0.001052 -0.000100 -8.803674e-04\n", 75 | "3 0.042482 0.029570 0.139154 -0.001528 0.000115 -1.009601e-03\n", 76 | "4 0.058426 -0.010219 0.159596 -0.002196 0.000515 -1.418501e-03\n" 77 | ] 78 | }, 79 | { 80 | "name": "stderr", 81 | "output_type": "stream", 82 | "text": [ 83 | "/home/finc/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n", 84 | " This is separate from the ipykernel package so we can avoid doing imports until\n" 85 | ] 86 | } 87 | ], 88 | "source": [ 89 | "# Load motion parameters\n", 90 | "motion_path = '/media/finc/Elements/ds000003-00001/sub-01/func/rp_sub-01_task-rhymejudgment_bold.txt'\n", 91 | "motion = pd.read_csv(motion_path, delimiter = ' ', header=None)\n", 92 | "parameter_names = [\"x\", \"y\", \"z\", \"pitch\", \"roll\", \"yaw\"]\n", 93 | "\n", 94 | "# Print firt 5 rows of motion parameters\n", 95 | "print(motion.head())\n", 96 | "\n", 97 | "# Add column names\n", 98 | "motion.columns = parameter_names\n" 99 | ] 100 | }, 101 | { 102 | "cell_type": "code", 103 | "execution_count": 10, 104 | "metadata": {}, 105 | "outputs": [ 106 | { 107 | "data": { 108 | "text/plain": [ 109 | "" 110 | ] 111 | }, 112 | "execution_count": 10, 113 | "metadata": {}, 114 | "output_type": "execute_result" 115 | }, 116 | { 117 | "data": { 118 | "image/png": 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w9YJtH1/w+jXgtc4zzTwcPXqU119/nX379qFSiRovQd/gUHIxr2w7y0BPB0orG7gq3BtPx6b88bFBrmz68xQeWneMu1cfYXqYJxklNQz0tGf6RRp1hXk7EtbO5KfAvIjeMhfw/vvvU1JSYpxIjYqKYuXKlWa2SiDoOKXVDTz2zQlC3O3Z9Ocp2Fu3/d8+1NOBH/88hXd2nmdHfB6pRdW8elOE2cv3BR1DiPsFrF692twmCASdyrM/xVFcXc/Ku9sXdgO2VhY8sWAoTywYSnmtBicbIRG9FRF3EAj6MAeSivjxRA4PzRh42SX5bVWhCnoPQtwFgl6KTi/zwa4kskpr2txfr9Xx1I9nCHK340+zBnWzdQJzI8RdIOil7E4s4LXtiXy0O7nN/WsPppNSpPRb6U3VnILOQYi7QNBLWXsoHYAtp3Np0LaseKzX6vh0XwqTB7q3aM4l6D8IcRcIeiFpRdXsOVfI6AEulNVo2J/Usp3HxmPZFFTW86eZIhzTXxHiLhD0QtZFp2OpknjvttG42KlbLIah08t8sieZiABnpgzq+1WfgrYR4m4i999/P/HxSpfjF1988ZLjly1bxnfffdfVZgn6IVqdng1Hs5g/3IcAVzuuGenLjrh8zudXEptWwj1rYkgrruGhGQNFtks/Roi7iaxcuZJhw4YBpom7QNBVnM2rpLxWw/zGkv8bIv2p1eiY+9Zelnx8iFNZZfx7wdAOtQQQ9B2EuF9AWloaQ4cO5e677yYiIoIlS5ZQU1PDzJkziY2N5YknnqC2tpbIyEiWLl0KwNq1a4mIiGDUqFHceeedxnPt3buXyZMnExoaKrz4LqKqXstLvyRQWt1gblO6jZjGhZbHBSsLL48PcWPtveN565ZRvH1LJPv+NYs/Cq+939Njy8/yXnyR+oTObflrHT4Un//855LjEhMT+eyzz5gyZQr33nsvH374oXHfyy+/zPvvv8+JEycAiIuL44UXXuDAgQN4eHhQUtK0CG5ubi779+/n7NmzLFq0iCVLlnTq+xEoPVM+2ZNCQUU9b90SaW5zuoXYtFL8XWzxdbY1bpsuMmIEFyA89zYIDAxkypQpANxxxx3s37+/3bG///47S5YswcNDWTfSzc3NuO+GG25ApVIxbNgw8vPzu9bofkpueS0APxzPZndi31/dUZZlYtJKjF67QNAePdZzN8XD7iouvJ292O3txVYzt7a2bjFO0Plkl9WitpAY4GbHkz+cYesj0y67r3hvIrOkloLKeuOanAJBewjPvQ0yMjI4dOgQAF9//TVTp05tsV+tVqPRaACYM2cO3377LcXFxQAtwjKCrie3rA5fZ1teXTKKgso6HlgbS52me9aoNAdN8XYh7oKLI8S9DcLDw/n888+JiIigpKSEhx56qMX+Bx98kIiICJYuXcrw4cN58sknmTFjBqNGjRLL8nUzueW1+DrbMDbIlTdujuRIagmPfXOiz94pxaaX4GRjSZiXg7lNEfRwemxYxpyoVCo+/rjFWiTs3r3b+PyVV17hlVdeMb6+++67ufvuu1uMX7NmTYvXVVVVnW6nAHLK6pgQonixi0b5kV5UzRu/nuN0djkRAS5mtu7ySC2q5otD6STmVzDAzQ5btSW55bXklNWSXVaHVq+npl7H1DAP0WNdcEmEuAt6LTq9TF5FHb4uTYs6L50YxFu/nePX+PxeJe4f7Erite2JWKokwn2d2B6XT51Gh5+LLX4utoT7OmFlqaKqXsstUYGXPqGg3yPE/QKCg4M5c+aKln8VdBMFlXXo9DJ+Lk0pgW72VkQFu/FrfD5/nzfEjNaZzvojGby2PZGFo/x46rpwvBxtLn2QQHAJRMxd0GvJKasDwK9ZvjfAvGHenM2rJKO47T7nl0NVvfayjzmeUUpmyaWvLcsy38Rk8OSPZ5gx2JM3bx4lhF3QaQhxF/RacsqUHPfmYRmAecOUsvsd8XlXdP7diQWMenYH+88XmXxMZZ2GpSsP8/j3p9odU1hZz95zhTyy/gSPf3+aiaFufLh0DGoL8d9R0HmIb5Og12IoYGoelgEY4G7HUB9HdsRfWeHYB7uS0Ollnt58plW/9Pb48UQONQ06DiYXt7lC0pnscqa+8jt3rTrCltO5/GPeYNbeO+GSa5sKBJeLEHdBryWnrA4Ha0ucbFoXLV070pcjqSWs2KusUpRUUElaUbXJ5z6aXkJMWilXhXuTUljN6gOplzxGlmW+OpxBoJvyY/P90exWY9bHZCBJ8NX9E4h98ioenh2Ghch8EXQBQtw7SHBwMEVFyu26g4PIOTYHOWW1+Lm0HaNePnMg10b48uLWs1z99l6uenMv934eY/K5P9mTgoudmndvi+SqcC/e2Xmeb2Iy0Onbz58/kVlGQm4Fy2cMZPJAd747lom+2fh6rY6fTuYyf7gPkwd54GpvZfqbFQguEyHuF0GWZfR6027HBd1Pbnldi+ZZzVFbqHj31tHcPmEA9Vo9Uwa5k1JYTXFVfeOxtazcl8Ifv4hl5b4Uo2jr9DKr9qfya0I+d04Mws7KkudvGMFQH0ce//40t644hFbX9nfiq8MZ2FtZcH2kP3+ICiCzpJa3fzvHmgOp5JXXsetsAeW1Gm4cE9A1H4hA0AwR6LuAtLQ0FixYwKxZszh06BCPPvoor7/+OrIsc+2117YoXhKYl5yyWkb4O7e730Il8eLikQAcSS3hQNIhTmSWMXuoF7etiCatuAZvJ2u2x+Wz5XQuI/ycOZpeSnxuBTMGe/LA9FAAfJ1t+f6hyXy0J5lXtyVyOruc0QNaNu4qqqpn08kclowNwMHakquH+/K8fQLv/p4EwPu7kvB1tsXL0ZopA8XqSIKup8eK+75vz1GU2blVnR6BDky7efAlxyUmJrJ69Wr++9//MnHiRI4ePYqrqyvz5s3jxx9/5IYbbuhUuwSXT51GR3F1A37OpqUOjvR3xkIlcSyjFH9XW9KKa3j+hhHcMWEAm0/m8OxP8SQXVBHkbs+bN49i8Wj/Fg3hJEni5qhAXt2WyKGU4lbi/mV0Bg1aPfdOCQHA1sqCXf+YSU2DluKqBh7+6hins8t5cHooliIrRtAN9FhxNydBQUFMnDiRTZs2MXPmTDw9lV7ZS5cuZe/evULczUx6cTUf7VYmSv1d2w7LXIitlQXhvo4czyjDsXECdm64N5IkcX2kP4tG+V1ycQsPB2uGeDtyKLm4xcLTdRodX0SnMWuIJ4Oa9XxxtlXjbKvG19mWTX+eypqDaSydOOBy365A0CF6rLib4mF3Ffb29oBo09sTqazTsPC9/dRp9Nw6LpBrRvqafOzoQFc2HstCo9MT7uuETzOv39RViyYNdOebmEwatHqsLBUPfPPJHIqqGrhvami7xznbqXnkqjCTbRUIrhRxf3gRJkyYwJ49eygqKkKn0/H1118zY8YMc5vVr0kvrqGiTsubt4zi5ZsisFFbmHzsmCAXqht0xKSVMmtIx1YumhjqTq1Gx8msMgB+OZ3L/22OY5ivE1MGiVi6oOfQYz33noCvry8vvfQSs2bNQpZlrrnmGq6//npzm9WvMRQGhXjYX/axowOb4uSzhnp16PoTQ92QJDiQVMTuxAI+2JVMZKALH98xVqxZKuhRCHG/gAsbh91+++3cfvvtrcalpaUZn4t2vt1HZolSlRrganfZxwa52+Fqp0anlxkd2LGOkS52VoT7OPHe70r16q3jAnn2+uFYW5p+ByEQdAciLCNoQWZJDXetOkJRYz54TyOrtAZHG8sOLaUnSRJ3Tgrm/mlXlrEye6gXKgleWDyCl24cKYRd0CMRnrugBeui09l7rpDfEwq4eVzP6xueVVpLYAe8dgN/m3vlE/V/nRPGsinBeDhYX3qwQGAmhOcuMKLV6fnhuNIP5Uhaz1wLNrO0hgAT0x+7CitLlRB2QY/HJHGXJOlqSZISJUlKkiTpiYuMGydJkk6SpCWdZ6KguziQXExBZT3OtmrjQsxdSXpxNV8cSjN5vCzLZJXWdijeLhD0Ny4p7pIkWQAfAAuAYcBtkiQNa2fcK8D2zjZS0D18fzQLZ1s1D04PJb24hoKKui693js7z/PUpjiTF9UordFQ06Azdl0UCATtY4rnPh5IkmU5RZblBmA90FY+4F+A74GCTrRP0E1U1mnYHpfHwlG+TBnkAXR+aKZOo+P7o1lodXrqtTp+bey3fiDZtMUwDKsbCc9dILg0poi7P5DZ7HVW4zYjkiT5A4uBjzvPNEF38t3RLOq1em6OCmS4nxO2agtiUjtX3DefzOHvG07yTWwmB5OKqazTGnPGTSGr1JAGKTx3geBSmJIt01ZlxoV1+W8Dj8uyrLtYIYckSQ8CDwIMGCB6bPQUdHqZ1QfSGBvkSkSAkv89JsiFI2mlnXqdo43ne29nEuNC3HC0tmTGEE8OJRej18uoLrFohaGASYi7QHBpTPHcs4DmOXEBQM4FY6KA9ZIkpQFLgA8lSWrVXUuW5RWyLEfJshxlaMbVk3jqqad45513jK+ffPJJ3n33XebMmcOYMWMYOXIkmzZtAuDVV1/l3XffBeCxxx5j9uzZAOzcuZM77rij+42/AnYm5JNRUmPsaAgQFeTG2bwKtsfldVqPndj0EvycbcirqOOnkznMHebNzCFeFFc3kJhfecnjs0prcbFTGxt/CQSC9jHFc48BwiRJCgGygVuBFiWbsiwbVUGSpDXAz7Is/3glhu1as4KC9JQrOUUrvIJCmbXswXb333fffdx444088sgj6PV61q9fz8GDB1m2bBlOTk4UFRUxceJEFi1axPTp03njjTf461//SmxsLPX19Wg0Gvbv38+0adM61e6uZtWBVPxdbJk/3Nu4bcnYADafzOGPXxxlfIgba+8df1l9XC6ktLqB5MJq/jl/CAeTiziQVMyCkb6M8HcClNBMZkkNSYVVLBjh22Z7gZ6QBikQ9BYuKe6yLGslSXoYJQvGAlgly3KcJEnLG/f3mTh7cHAw7u7uHD9+nPz8fEaPHo2bmxuPPfYYe/fuRaVSkZ2dTX5+PmPHjuXo0aNUVlZibW3NmDFjiI2NZd++fUaPvjeQX1FHdEoJ/5w/pEXVZqCbHb8+Np21h9J57ud4vj6SwT3NPPvL5ViGEpIZG+TK3GHerNqfyvTBHlhbWhDqYc9bv56jukEHwKvbElk2OZj/WzS8xTmySmsJ8xJLGgoEpmBShaosy1uBrRdsa1PUZVleduVmcVEPuyu5//77WbNmDXl5edx77718+eWXFBYWcvToUdRqNcHBwdTV1Rmfr169msmTJxMREcGuXbtITk4mPDzcLLZ3BMMk5TBfp1b7LC1U3DMlmB3xeXywK4lbxgViZ9WxoubY9FIsVRKjAlywtbLg5ZsijPvmhHux5mAa/5w/hOsj/XhhSwJfHcng7/MG42ij5tVtZ9kRn09yYVWHuzkKBP0NUaF6AYsXL2bbtm3ExMQwf/58ysvL8fLyQq1Ws2vXLtLT041jp0+fzuuvv8706dOZNm0aH3/8MZGRkb2qO2B+Yy67t1PbKxpJksQ/5w+hqKqBNQfTjNtf3JrAn788ZnI8/mh6qZKFY9U6tPOP+UOI/vcc/jxrEAGudtw3NYQGrZ7fzxaQkFvBh7uTcbVTc/ekYJZOCLr8NykQ9ENEb5kLsLKyYtasWbi4uGBhYcHSpUtZuHAhUVFRREZGMnToUOPYadOm8cILLzBp0iTs7e2xsbHpdfH23HJF3H0vslzd2CA3Zg/14pM9KdwzOQS9LPPFoXRqNTpmH/PiprEXX/C5QavnZGZZu8JsbWmBtUOT6I8Z4IqXozXbzuQRm1aKlaWKT++KwsXOqgPvUCDonwhxvwC9Xk90dDQbNmwAwMPDg0OHDrU5ds6cOWg0GuPrc+fOdYuNnUl+RR1Wlipc7C6egfLH6aHcsiKan07mYK1WUavR4etsw/+2xBPsYc/+80WMC3Fl8kClAOpMdjnBHvY4WFuy5XQO9Vo944JdL3oNAyqVxPzhPmw4momlSsV1Eb5C2AWCy0SIezPi4+O57rrrWLx4MWFh/WNJtNzyOnydbS4ZShof4sZgbwfWRqfh5WiDr7MNq+8Zx3Xv7uemjw4C4Ghtyea/TOVEZimPfXMV8dp7AAAgAElEQVSSgZ72/G3uEP698TRRQa7MCfe+6DWas2CkD19EpwN6EYoRCDqAEPdmDBs2jJSUzk2/7Onkl9e1G29vjiRJ3DkxiKc2xSFJFTw4LZShPk68fWskeeV1RAW7sWz1Ee5ZfYTsslpGBTiTWVrLn786hp+zDR/dMda45qgpjA92w93eCk9Ha8YM6NjCGgJBf6bHibssy71qQvJK6AkLcOdW1DJmgGnhksVjAnj5l7NUN+hYFOkHwHURfsb9b90SyT2rYxjoac/aeydQUafh7d/Oc9/UEDwdL69FrqWFipV3R+FoY9lvvg8CQWfSo8TdxsaG4uJi3N3d+/x/aFmWKS4uxsbm0l5zV9qQX16PjwmeO4CDtSX3TAkhJq2kzdTJWUO82LB8EsHu9jjbqXG2U/PGzaM6bN9oE390BAJBa3qUuAcEBJCVlUVhYaG5TekWbGxsCAi4eKZJV1JS3UCDTo/PRTJlLuQf84dcdP+4YLcrNUsgEHQCPUrc1Wo1ISEdr4IUXB55jTnupnruAoGg9yCKmPoxeY057pfjuQsEgt6BEPd+jNFzF+IuEPQ5hLj3Y/LK61BJ4CkWexYI+hxC3PsxeeV1eDpat+gGKRAI+gbif3U/Jq+iDh9n0R9dIOiLCHHvx+SV1+HjJEIyAkFfRIh7PyavvA5f4bkLBH0SIe79lOKqeirrtfi7CHEXCPoiQtz7KScyywCICHA2syXmoayujBpNjbnNEAi6DCHu/ZQTmWVYqCRG9lNxX/7bcl6NedXcZggEXUaPaj8g6D5OZJYx2Nuxw2ui9nYyKzNRScK3EfRdxLe7H6LXy5zILCMysH/2SdfpdVQ2VJJVmWVuUwSCLkOIez8kpaiayjoto/upuFc0VCAjU1pfSlVDlbnNEQi6BCHu/RDDZGpkP13hqKy+zPg8uyrbjJYIBF2HEPd+yInMUhysLRno6WBuU8xCeX258bkIzQj6KkLc+yEnMsuICHDGQtW3V7tqj+aee1aVEHdB30SIez8jq7SGuJwKJoS4m9sUs9FC3IXnLuhqGqpBU9vtlxXi3s/4JiYTgJvG+pvZEvNhCMsEOgYKz13Q9Xx9G2z6c7dftn8mOfdTNDo938RkMnOwJwGuduY2x2yU1ZdhKVky1G0o50vPm9scQV8n7zSoLEGWQZKgtgxsuz6ZQXju/YidCQUUVNZz+4SgFtv3Ze3rV6X4ZfVlOFk7EeAQQHZVNnpZb26TBH2V+kqoLYHqAqjIBr0e3h0Nvz7d5ZcW4t6P+PpIBj5ONswa4mncllmRyZ92/omfU342o2XdS3l9OS7WLgQ4BqDRayioKTDuK6gpQJZlM1on6FOUpjc9zzkOeScVsfce0eWXFuLehyiraeBAUlGb+yrrNBxIKuL60X4tVl46V3YOUMrx+wtl9WWKuDsEAE2TqjlVOcz/bj4fn/zYnOYJejsH34P1S5XnZRlN27OPQcpu5XnIjC43Q4h7L0KWZX6Lz6deq2tz/783nmbpysPsiMtrte9AUhFavcysIV4ttieXJQOKsF0J7x9/nx1pO67oHN1FWX0ZztbOBDg2invjpOqerD1oZS0rTq0gsSTRnCYKejNJOyHxF9DUQVmj5+4UoHjuKbvBaxg4ene5GULcexEHkoq5f20sq/antdqXXFjFtrg81BYS//r+FLnlLVOvdicW4mhtydgg15bHNYp7bnXuFdm2LmEdPyb9aHydVp6GRq+5onN2FeV1SljG194XlaQio0LxrvZm7cXX3hcnayee3P8k/3fw/7h9y+0U1bZ9NyQQtElpGsg6KEpUwjJqexg0W/HcM6IhdFa3mCHEvRex8ZjiYa6LTkenl9HrZRLzKpFlmRV7UrCyUPHVAxNp0Op5+KvjVNVrAcXj351YyNQwD9QXLIbdGZ57ZUMl1ZpqMioVkaxoqODGzTfy4YkPO3zOrkKWZWNYRm2hZpTnKLambqWqoYqYvBjmDJjDkxOeJLE0kc3JmzlddJozRWfMbbagt6DTQnljiDM/XgnLuAaB3xioLwdtHYTO7BZThLj3ELKrssmsaD/uXdOgZVtcHgPc7Mguq2XX2QJe2XaW+W/vZcE7+9h4PIubowIZF+zGa0tGcSKzjFtXHKKwsp7E/EryKuqY2TiReiz/GAU1Bej0OlLLU7GULCmuK6ZeV98h2w1ef3ZlNlq9lpSyFDR6Dd+c/YZqTXWHztlV1GpradA34Gyt9LFfNnwZ2VXZPB/9PPW6eqYFTGNe8Dw237CZLYu3AKL/jOAyqMgGveJUURCnhGVcBoD/GGWbyhKCJneLKULcewjPHHiGv+35W7v7t8flUdOg4+UbR+LtZM0zm+P4ZG8KM4c6o9XLWKgkFo614auEr7hquBuf3jWWpIIqFryzl2e2HABVDTOHeFHZUMn9O+7nlSOvkFWVRYO+gTHeyhcvt6pjoZm8aiXGr5W15FTlGO8GKjWV/HD+hw6ds7PRy3o0eo2xgMnFWskznhk4k1DnULambsXW0pYo7ygAQpxD8LH3wdrCusOfi6AfYoixSyrIj1M8d5cgJc5uYQ0B48G6e3o6mSTukiRdLUlSoiRJSZIkPdHG/qWSJJ1q/HdQkqRRnW9q3ya1IpWzJWcpqSsBlMwXja4p/3rjsWz8XWyZGOrO7eODyC6rJSwkhVMWf+Wz+0I4/J+r2JL5BS8deYlbf76VavURbp4bh8r/XeIt/4X7wC/wdrJhX9Y+NHoNe7L2cKrwFABT/acCkFPdsdCMQdwB0ivSSS5PxsbChkjPSNYlrENr8GTMyIuHX+SPv/7R2HrAIO4qScWy4csAmOQ7CSsLK+MxkiTha+/b4c+lX1FdBEWiIIzSNOVxwCTIjIH6CiUsY6GG+S/AzMe7zZRLirskSRbAB8ACYBhwmyRJwy4YlgrMkGU5AngeWNHZhvZl6rR1xlzrmLwYKuo0zH5vHTd8/AuVdRp2JuRzIKmIxaP9Uakklk0O5sFpITh470Gj17A/Zx/OtmqO5B4hzDWMsvoy/rP/P2xK+4KBng6M9ZhGg2UqCcUJ/JbxG2qVmnpdPavOrAJgiv8UQPHcqzXVfHTyIzQ60ydDm4t7RmUGKWUphDiHGEMee7P2duKn1THOl54nNi/WOC9gCMsAXBd6HTMDZ3LLkFtaHefn4HfFmUT9gq3/gLU3mNsK81OaBpIFhM2Fhkplm8sA5XH8A90WbwfT2g+MB5JkWU4BkCRpPXA9EG8YIMvywWbjo4GAzjSyr9NcPPZnRXMs0YMGr/dJqRzBzZ84kVxQxQh/Zx6cEQqAs52auWOr+Xp7IhISh3IOMTtwNllVWTwx/gkWDlxIZkUmoS6h2FraUtFQwexvZ/Nlwpfsz97PDYNuYH/2fpLKkvC19yXEOQSVpCKnOoctKVv48MSHjPUay3jf8SbZn1edh4+9D1UNVaRXpJNSnsIY7zHMCJyBvdqeA9kHmD1gNrIsk1edh6+Db5d8jhejqLYIGZmdGTuBJs8dQG2h5r3Z77V5nK+9L2dLznaLjb0WvQ6Sd0FdGdSUgJ2buS3qHmQZGqrA2rFpW2k6uASCT0TTNpeg1sd2A6aEZfyB5jN9WY3b2uM+4Je2dkiS9KAkSbGSJMUWFhaabmUfx1BApNc6sC15P+viNiCpNHh65pCYV8FgHwc+uXMEDtYWxmM+j/scNxs3Fg1cRExeDAdzlN/XcT7jcLJyYrjHcGwtbQFwsnJifvB8NiVvolZby9yguVwdfDUAoS6hqFVqvOy8yK3KNZ6nuK7YZPvzavLwtfcl0DGQsyVnya3OZaDzQCxVlkR5R3E47zAAOzN2Mu/7eRwvOH7lH9plYng/hrsIFxvTenv4O/hTUldCrbb7u/r1GvJOKcIOUGjGH8LSdKW8v6uQZTi7BQx3tee2wWuDoCS1mQ1p4BoM3sObthk8927GFHFvq+l3m/XZkiTNQhH3NgNLsiyvkGU5SpblKE9Pz7aG9EsSipQvh33DBOrIQ3LegwoVpQ35rH8onI/uGsz1P8/jps03seHcBl46/BJ7svZwy5BbmBk4kxptDWvi1uBi7cIgl0FtXuMPg/8AKEIf5RPF1SGKuA90HgiAn70fmZWZHM5VhNgQ+zeFvOo8fOx8CHIK4mThSUD50QCY4DuB9Ip08qrz2JS8CYCvEr5q8zy/pP7CRyc/Mvm6plKnrTNm7Rgena2cL3aIEcNdhphUvQgpe5qeF8S3P64rqSqA98ZC9Addd43zO2D97ZDwk/I6I1pJbTzzfdOY0jTFU3fwBls3sHHuliZhbWGKuGcBgc1eBwCtgpCSJEUAK4HrZVk23e0TEFeQiqy34pGJigBLllXcNfwuAMrlZGIKDlCrraVWW8tzh57ju3PfMS9oHncMu4PxvuNRSSrSKtIY5zMOldT2n3SU5ygiPCNYOHAhapWacLdw/j7270bR93Xw5VTRKao0ypqixbWm/Qn1sp786nx87H0Y4DTA2IQr1LlJ3AF+Tf+V/dn7sbW05bf039osDNp4fiOfnvq0wymZ7WHw2v0dlBtOe7U9agu1Scf62fsBlzfZ/MzBZ/jm7DeXaWUvJnUPeAwBaycoMJPnnhUDeg0cXaN42F1BXGPmV95p5bGwsYr5zEblsb4KaooUz12SwC8S3MO6xhYTMEXcY4AwSZJCJEmyAm4FNjcfIEnSAGAjcKcsy+c638y+TXpFJvoGN+aHjcHRyhEfex/+FPknrFRWnCw4ya6MXfjZ+7Fl8RbWX7uevbfu5Y2Zb+Bk5YSTlRMj3JUmRON8xrV7DUmSWLdgHU+Mf8L4etmIZQQ7BwOKiOllPSpJhYPawWTPvaSuhAZ9Az72iucOYKmyJNBR8QfCXMJws3Hjo5MfodVreWbSM2hlLd+d+67VubIqs9DoNcQXd673Z/ihMoSimsfbL4WfQ6O4mzipqtFr2Jy0mW/PfXuZVvYyChJgy9+hMg/SD8HAWeA5xHxhmexjymNxEmQe6fzza+vh7FblueHupDABLKyUfPaCs01pkK7ByuOi9+GmlZ1vi4lcUtxlWdYCDwPbgQTgW1mW4yRJWi5J0vLGYU8D7sCHkiSdkCQptsss7oMU1OZgJXvi4WDL05Oe5tnJz2Jractwj+FE50ZzKPcQMwNnYqGyYLjHcOzV9i2On+g3EYDxPhefAJWk9pfVM4QfRniMwN/B32TPPb86H0Dx3B2V2GKwUzCWKkvjNSf4TKCyoZJAx0CuCbmGyX6T+frs1/wv+n+si18HgFavNRZDHcs/ZtK1TcVwlzAzcCY2FjYtMmUuhaetJ5aSpcninlmRiVbWcq70nMmfYYcoTTfL6j5GYldBzEr4aDJoa5VGWJ5DFdE3BznHwH2QUup//IvOP3/KbqXC1MFbyV9vqFH+BqPvVHLa4zY2pUEaxN3ZH9xCOt8WEzEpz12W5a2yLA+WZXmgLMsvNG77WJbljxuf3y/Lsqssy5GN/6K60ui+hCzL1OgLcLNWxPXq4KuZ7KdUsEV6RpJYmki9rp5ZA9rvR3HXsLt4bfprDHQZ2GE7DOGHyX6TcbNxM9lzN6RBNvfcDSEZA4bQzNXBVyNJEveNuA9rC2t+TvmZV2Jeoai2iLzqPHSy0hCtsydcDWEZH3sfrgq6ipEeI00+1kJlgbe9t8lhmZTyFOPzI3ld4EGC0pDqo8kQ3cnzE4c+aO311pbCt3fB9w/ArpcUDxYgKxbcQpVMGckCgqeAV7gSlqjupl481UXKZyHLiuceNAWGL1bCJw2dXBkd9yNYO0PUvUp7gexYQIbQGRA8FY6vg2NrlbEGcTczokLVTPx8KosnN8WQW1WALGkIcmqdPTrKU6kFc1Q7MtZ7bLvncrZ2Nk6QdpQRHiMY6z2Wa0Ouxd3W/ZLZMq8ceYU//vpHY2m+j70PLtYujPcZz4zAlu1MZw+YzczAmcb4/njf8exYsoO3Zr4FKP1tDJ0ZAx0DOV5wvFMX0DB40G42brw07SX+O/G/l3W8v4O/yROqhupce7W9cXK60ymIV1LwipM775zaBtjxX4i+oB9Q7GqI3wTpB2HPy03dDvNOQ/gieHA33PWjMnHoObTRvg5677IMdRWmj/90Fvz8KJSkKNk6/mNgzJ3KZ/PNHcoka2egqYPELTD0GvAbrWwzxNk9h8K4+5XVlc5tU4Td1rXdU3UnfX6ZvTdj38TfwZ9bhrYuUDEXCbkV/Gvna0hOh6nc9ScAhnoEtxo3yksR96kBU1GrTJsA7CjO1s6suXoNgEme+56sPWRWZpJUloS1hTWu1q5IksRn8z9rNdbVxrXNPPIwV2WyyXAOgIUDF/LhiQ9JLks27j9TdAZvO2887TqWYVVcW4yTlVOL6tPLwdfel0M5h0wam1Kegq+9L0PdhhKdG02ttpaPTnzE4rDFhDi3cYtekgKHPoSrX1KqGI+vU8T09m+VSbm2MEzoVXRiz5vyTJD1kHuqaZtOq4ReQmbAHRvhtVBI+hWc/JXJy4AoJexgCD14hSuPhWchZNrl25CwGX5YDo/FXTpXvqZEKe0vzwaPxklL/7HgMxKuewu2/Rs+GA+e4Ure+cJ3QG17+TYB7HsD6sph9B1NXnn8JlCplbsXzyEw7HrlR0Bl0f7frZvpc577wZyDbE1RJj5qtbV8kfAF7xx7x+wNrAoq61i5L4XNJ3N4+KtjqO3TUVlWs7NQubUe49c6hdHD1oOnJj7F8lHLW+3rStxt3anV1ra79F5VQxWZlZmoVWoKagrwsfe5aDy/3evYuONi7UJSWRJZlVlYSpYsCF4ANIVmThee5s6td/Lu8Xc7/H6K64pxt3Xv8PF+Dn4U1ha2W7W7O3M3W1KUJmOp5amEuoQywXcC2VXZPLDjAVbHrWbl6XYm1uI3QcynkN/YefLsFji/gzPJv3Ag+0DTuNjVkNC4WpZR3K+wcvbQB3DkU+W5IVe7JLnJez77s/IDMmE5WFgqrWqTdiqZKQD+F0RfHX2V0EVHPffMI6CpUX7wLkVxkvIo62D3K2Bpqwg5KKGTB3bBwNmga4BT3zQtknG5FCTA/rcg4hYl/OLkr7zH2hIlxt8860pt0/K1men14q6X9Ubh1uq1PH3gaZ6Lfg6NTsOpwlNo9VqzN7Aqr9Ww9LNdvLzvK/769TFSi6qwtsvDxsIOlWUVsiwxcUDb+ek3D7m5VQy7q3GzUbym9kIz50qVhKh/jfsXtpa2+Np3rOJUkiQGugwkqTSJrKos/Bz8CHIKwsPWg+1p28moyOCfe/+JVtZeUZVocW0x7jYdF/dAx0BkZKJzo9vc/1rMa/wv+n/UamsVcXcOZaKvMsl9svAkAQ4BykIgjT12UspSmpbyMyzDZkghLEjgsI019xx8kv/s/0/TuF0vwM5nleedJe4xK+FIY6eQ0maFOIYfmsOfKDnbg+crr8PmQmWuMmHp5A9OF/zdJQm8hkLuyY7ZY+hNU27CqmBFjUl5AyaBrh58I5QfIAPew2DJKli2BSxtILUDLTBkGX5+TKlAnf+isk2SmgqUvIZe/jm7kV4v7uvi1zF3w1xyqnLYnbmb/Jp8qjXVHC84Tmx+LCpJRbhbOOsS1hFXHMfDOx8mJi+m2+wrrW7g/rUxZKtXY+v/DR/d68Nn9w2kVlfNI2P+gq3khRVuOFp38JaxCzCKezvZHgahnT1gNivmruAfUf/o8LUGuQwiqSyJzMpMAhwDkCSJm4fczJG8I1z7w7XkVecxwWcCyWXJHV7840o993nB8whxDuH56Odb3QGmlaeRUZlBlaaKDYkbqNPVMdB5IKHOocwPns8T45/g71F/p7y+nKP5R/k1/Veu33R902SrIX2uMAEaajhZk8PD3p7oZB0ldSVKz6HqYqguVAStJEURX5Va6V1yOTHq5ug0SlijOFkJJ5SkKhOjoIRmChMh46DSD0XVuH3QVcpjQbwSAmmLwVcrk415HeiBbxDsMlPE/bzyGVzzuvL6wrsIA2obCJzQVGh1fB2svMq0XPjaUsg4BJP+DPYeTdu9G1trGe4Ueii9XtyTypKo1FTy4uEX+ersV3jbeaNWqdmbtZfYvFiGug3ljxHKxN+tP9/Knqw9rD+7vsvtqtPoePKH00x8aScnSn7HwkG5VS3QxKFTK7HSUZ6j+HbRKlYteKfL7bkcDELYXtw9sTQRNxs3PG09ifSKZIjbkA5fK8wljCpNFedKzhnXNH1o1ENsumETtw+9nWcnP8sNYTeg0WtIL09vdXx+dT7Lf1t+0dWSrtRzt7aw5rnJz5FXncdbR99qsc/QzsBebc9nZ5T5hlCXUCRJ4vUZr7M0fCmT/SZjbWHNr+m/8t4xZe4hsShOOUFzz734PN862mONxGtFSjl/QklCy9zxI58qE4bBSidPo/deVXh5xTtlGUrfcVnX9KPhOQTsvZR2Aqe/U1L8Rt7cdIyjT1PPlIB2xHTsMiVEctiEdWjLMuGtEUroo/mSdOVZlz62OEmJd/uMUOYDpj7a/tiQ6UouelUB7H1dCStVm9D+pLKxIZ7bBXfOBs/ds+Pf++6g14t7XnUeKknFnqw9xOTFcNvQ24jyjuL3zN85VXiKKO8oZgbOZJLvJBYNXMRVA64iOjcanb7tdUg7i/VHMvjycAbTR1bhEbSVSM9Igp2COZx7mISSBCwkC8Jcwwh2CSLSu2d1SDYIYXvifrbkLENch3Qozn4hg1yVcJRW1hrXNAUlnfLfE/7N9YOuZ7DrYED5UQFaxL43JW/iQPYBjuS2nXZYr6unSlN1RZ47QKRXJEvDl/JN4jfG3H6Avdl7Geg8kOtCrzN+XheG0ezUdkzym8SGcxtIrVDCH2kZ+5Q0QkMIoiABCs6SplYz1N6fSVUVSEhKQVdhYwzb1lWJvYPiIYMSEy9JgTeGQPLvpr+h5v1QChKUsIxriBLeyD0JZ76D4Gmt1/oMm6s8tucp27nBqFvg9AbljuNiZB1R3n/iVuU9GDKk2hP3qsKmDJiic00TqYPmgINX28dAUyfGnc82hZ8MOekXo7IxQ8rxgvDToKuUz8bwA9tD6f3iXpPHjIAZDHEdgrWFNTeF3cS0gGlkVmbSoG8gyjsKC5UFK+at4IWpLzA3aC4VDRUklCRQo6nhzdg3L6uPiino9DKfHUzAa/Aaouuew8pCzXNTnmOC7wSO5h8lriiOEOcQbCxtOvW6ncXFwjIavYak0iSGunVOvLF5L5zm4t6cEKcQLFWWnCs9R1FtEdO/mW6scP01/VcAo2ieKTrDo7seNf4AGN5DK8+98FyTZ9YWtWWtNt0wSGlpa2iuVq2p5mj+UaYHTDdWv7rbuLdZJDVnwBz0sp4R1p5E1NWTVpmuiIeuQVk8uTwDOSuWVCs1wX4TsFOpCbGwJ6E4QQmRWDspXrS2VlnNZ9Ac5cQVOZB1VPHADbF4UzBOWkpKmKc0Tcl68YlQXpekwMglrY+LuhcmPQwB7VdDM2G50nMldpVpNmREN4VkHP2gPKPt8T8+BF8uUbJ4SlKbxP1S+EaClaMSkjFkTBnEvaqg6e7pQgzfD0eflttdBsCyn1uGanogvVrcDS1k/R38eX/O+6yctxIXGxemB0wHQEIyrjJkwFBQczDnIF8mfMnquNX8lPxTp9q1Iy6PPF00tRZneWzsY/xy4y+EOIcwwXcCNdoaDuUeuqJQRldjZWGFo9qRkroSksuSeevoW8aJvbTyNBr0DZ1mv7O1M162itdlCMtciNpCTahzKOdKz7EtdRtVmipWnFpBSnmKMf6fWq6I+/a07ezM2Mm5MkUsDOLuYXvBf8T1t8P2/7RtVN5peDVUyQxpxmDXwXjYehjTIqNzotHqtUwLmMYY7zF42XkZ70QuZFbgLCI9I3lc7UeIRkNqfWmTqAxRfhiKE3+iUqUixH0IBE0mvK6W+JJ4KEjgE29/Nrs05k97Dm1qI1uRA/mNom6KN2qgJBmsHJQVgpJ3KWLsFgK+jXeRKjWEL2x9nHOAsuiE5UXSSr3CYfAC2Pd6y9TKCyk2iPvhptBT6Mz2Pffi88pdxfntSiqmqX1bLCyVIitQctKh6bP66VFYfY1yF3UhRs/dp/W+XkCvFveKhgpqtbX42PvgY+9DpFckAEFOQQQ5BTHYdXArL8rd1p1wt3B+z/idz+M/B2B/9n5A8fru3HonlYYm+yby/vH3+fe+f/Pm0TcprCnk030pOLjFEeQUxD3D78FObQfAOG/F29HLesLdevZkjKGQadWZVaw6s8rYGsAgpp3luQPGytr2PHeAIa5DOFd6ji0pW3BQO5Bbncu/9/0bUETXUBlqsO9ciSLuhlh8i7CMXq/Ed9trcnXqW8UTbt7tDyW7Z7LfZA7lHkKn17E9bTuOakcivSJRSSo+nPMh/53QdoGUs7UzX1zzBZGleQRrtBShpar4HOmWlvzPshoNkNqg3EGGOIXAkGsIryiioKaA2LJzfGBRzaf5BxRB9o1UxNXeSwnL5DfG78va8UDboiRFiSV7D2v6cTCEZUAJPVxJMc6i95SuiN/coUxMtmcDklLWH78JnAPBc7Ayvr6q5Vi9vml+YfdLyqPHYNPtCZunLHM3Yblyd2AQ99wTUJGl/MBdSGUe2Lh0PD/ezPRqcW9e+n4hr01/jRemvtDmcRP9JhJXHEd5fTkDHUdxNP8oNZoaPo/7nBOFJy5Z/i7LstGTLa8v55NTn7Anaw9r49Zy2w9/53h2FjrrJOYFzWsRl3axcTGKYk/23EEJzeRX57MrQ/nSp1cowpFYkoi1hbWx1UBnMNZ7LEFOQThaObY7ZrDrYApqCjhTfIblo5YzyGUQ8cXxjPQYyWS/yaSXp6PT60gsUeLyhnRNQzpni7BMTbESDilJad3/W5Yh/kfleeIvSgigGZP9JlNWX8bPKT+zLW0bSwYvMRaYDamvI7iu7doA47kLEwnRKtdMy47mWydHvsk7wAl7R1LVynlCnEMgfCHDGhoAeNpBheN5/UsAACAASURBVAykVaSTe8vnMOdp5XxOvorgGTJT2gsvtEVxsiLuXs2cDLcQReCj7oNp7a/naxIOnnDz54p9v/1f22NKUpTyfVAycDwGKwIPrb33miLlbwZN4SePtu+S2mTsMnjsjLLknWuwIu61pU2FYG31o6nMbR1v70X0anHPr2lqWnUh4e7h7QqooXeLrXYYp89EodFr2Jmxk98zlAkpw9qibSHLMreuiOav608ATSLyyrRX8dQuIl97nIiI/cjomRc8r9XxE3wmICEx1LVn58i627pzsvAklRrlLiajQomDni87T6hzqLExWGdw/8j72bho40XHGCZVVZKKBSELuHfEvQDMDZpLiHMIDfoGThSeoLRe8RINk6/G1gO2zSoeKxs9QG1t0623gZzjSiZJ2DylUCWzZW77JL9JSEg8H/089mp7ox3IMqy/Q0mzyzqqpBqe/g5S9zVlsVRkQ30FIX5Kg7fUrIMctFcWSz7i6kOa2hIbSY23vTc4+TGksQdOplrNRGfl/R/SVzRNcjr5K0JXlac0zCrPvPhiFSe+hs1/UX6wytLBfSB4NWZ+qCzBeYCSx33dmxBo2ipcFyVwPIz8g1Kqr6lTtpWmK59NXQVUFyjVr06Na/9cKO56XdNxBrEfrBS5YedxeXcWKoumSVeDuOc3dnf0HKoUj104AVyZ12tDMtDLxd3ouduZ/gfILqtl53F7qJhITe412OkHocKK12Nfp0HfgJOVE6eL2p+Y2h6Xx+HUEn46mUNyYZUxDFBb5c3586Oxs3AhpW43AxwHMMS19Y/LAxEPsGLeCpNXAjIXbjZuyMjYWdphY2FDeqXiFSaVJhnbAnQWFiqLS7YGGOymiNsEnwl42XlxTcg1PDv5WW4ZcouxrP+XVGUBsIHOA0ksSUSWZfJr8nFUO2KNRZMX3rz4x1DpaCD+R0Xorn1TuY0/u6XFbjcbN8Ldw6nX1XPX8Lua/o7FycpEoK5emfT7eCp8fx98fh18NEXJSGmMKweOuBULWSZGX0WSpXJnd8TGmlS1mmAHf2NPfqdhiwnUaLCUZZ6d8B+8bL2Mk7nKAD9F2AHCrlI82wt/rAzoNEq2yLG1SgdDvbal5+4c2LIIqLOI+IOySPT57cpn8N4YpeGZIWvFfSAMUAq+/r+9946vo7zy/9/PzC3qvdiSi2S5yBVbGFzAJhgMNsVsIIABEwIkQNgvaXyTwJJsfqRCym5IsiRfliTLhhZaEjBtafYabJqNG+5FlmVZ3VaXbpnn98eZuVfdcpFVmPfLet17Z+beOZ5758yZ85znc8iYIDl9kGP51v3wsL3OibDn3iHjAceTkulMap78Bsps9dGLfiI5/C3PdtyuodyN3AeK8qZyPMrTdbCsB0pqmrno31bzxzUlzE+5nX/cfhUXFOaiW8ZT21rLpNRJXJR3EVuqt3QrXBUKW/zi9Z2MTY/D5zF4dM1+dtTuICM2gzU72ojxxHDnzNsBmfjSXalgsj85MntxMOOkMc4bdR6jk0ZzoP4A9YF6KlsqT0p9slcayqWKpQd7vjT1S9w5U7R4TMPkyglXEueNkxw10cqZywouoz5QT3lTOWsOrWFW9ix4/hb4i93Aub0mS+1eiXafuxWevRk2/VWiyZTRolG+Y2U08m6uhe0vcdHYi8iOy+bGyTdGP8cpQ7z+Gbk4BFvg2sdF07u+FN7+cSTH7x1/AblhzSvxMhZzTs45bLYa2emPIb/93ebky1lR18Bd9S3kjChibs7cjmW8STnRbQsvk8ee8u7bXxLHrwx44weyLG2cOHVfQv9J0+YtlLGBLc/Cm/fLRWXnK1HRs7QCmWUK4twTR8jxq9krF6Ij+0X9sc7+zrKmwuIfwpzbT9ym1DxAw87XJKc+/gLIKYL1f45+15YlF84hHLkPaeGw8qZyMuMyMZ0ZdL2gteaeFzajlOLNb53HuEy5HT5nfAYvvzWemNhtUupm+WkIPMfaAzsww9lsPVTHG9sqqGlqIycllr1VTfxhxZms3lXFCxtKKZy9nUmphbz6wWEuKMzm+skX0hSqjyggDlWccsgLx15IcH+QPUf3RBQPe2rld9xs+qtMq79rvWhy/M/3RLr1a1313JVS3D377m4/JiUmhVR/KrWttYxJHENRllRIPbvrWcqbyvna2GWw5nvgjbMH5g7LbEzTK06kYqvUdcekiLrgxfZYTeGlovRXsVUEqdb8Ctb9jlvu3sVNU2/qmJra947kq8dfAF/7BDz+qM5IzR5Y+1tx+HEZEJ9BnieeElrIMGO4YfINvFf2HlWmIr/9hTN1LNcnFcpnKcW8nHm8uPdFttZsxaM8jEvIIhYkusyxq8KOFMPY+V0P0oePSIXNpKXRCUZpBWAYcN53u07UOVWYHph2lexfh2Uw8+CH0UlQaflyIW09Kk7eMOWitfHJ6EBs1Q65QJp+KT+cd+fJ2ZRqjxeVrIUx8yUVdfZt8Pc75HssWCTjMlbIjdwHivLm8m7z7d3x7MelrN1bwz1LCyOOHeDcCRkE62YyM+lygkdn84NnJcf8paee47r/fJ+fvLKd+tYgY9Pj2Vxax9xxabxV8yuak56gLRxgf90+Eo0xVDcGuGzGSLymlztn3nnCCoaDhQWjFnDNxGtYOGohY5LGUNpQGhmsPGWRe/EaiTSdVELtfonUehDo6g0nNTMpbVIkP//49sfxGT7O32GXNAabJd9eXyYRWVqBOPfiNbL+q2vhX8rEGYGd31XR1Mzu/wFA1e4Vx/7xn0XrPNAsufUCW3Pfn9BRQGr2zTJBZ+9bkTRIXpw4jflJEzgz+0w8Si4UTmesCMufFI0UiNzx3fTqTSx/eTmP1dk54+yp4iBR3Q+qHt4s0+jP/ood8SqJ1p0c9Dlfg8mX9e1Anwgzro469n96WJ5/8oQ4Tl+8yAUv/Hb0mCWPlvEOf5K8rtohkXtSzqlRXHSUHbUVnW067Uq5w3jfvvAN8TJIGAaRu9Niridag2EeXrWXP6zey9l5aVx/dsdO5COTYylIz6S6ZCk/31BM0ahJ7DNimT2thVsL55CfGU9uipRCOVUy5/71bpqCTVxYNIkPmsO8tclDnM/kc5N6mSU3xMhJyOH7874PSGlpSIdYc2jNSQmFEWiGTU/CmTdLhObku+sOycSQ+jI54eoOHnckmZ+cz4bKDRSmFZLgSyA3IZdDjYe4ILOIhA//DuMXi1xt9W5JyyTlyInr9MFMGyedc9qTkCn54B0rRRXQmWhTs1ei463Py4WhqUZ0XgoWdW9cap6UFu55IzJlPS91AjTvY96Is4nzxjEtYxobqzZ2lQVuN0M0IzaDL075InVtdawtW8uuNnsAMHsaAaXwJY7smpZprYN//LM481krZBByyhUSmZ4Gadq6tjr8WVOImXUjFF7Kp4lpFMQkE9NSK801usPJu8++RcTNqnbJbyO551LZ4yIhW8TEQq1RnRiPX/a3+gGo3tNuApMbuZ92tNaRxsy98cU/fchv3trN0mkjeHhFEYbR9Qe9YEIm2w/X4zEVv1lexMys6TSrfZw7ISPi2EFSA1UtVTQEGrC0xUElGjVHjmZyweRsYn3HTg8NRZz2ee+Xvc/4lPE9NuGOEGqDf58u3Wvas/V56btZLPMKIiqA9YdksNMZHDyeyTg2jlN0Sk2d6H1J3VGpt176YHSfDYfFuaePl7uFA2tlOnl3TLpEKlI+ciR7leTpQS4M3jg48K7ksnv6DIhOnrEbWiyc/c9cljSRz824GYBzcs8hxoyJHOue+PZZ3+bH5/6YqRlT2d9SCdO+wOGChcx7ch7vpmZ3jNzbGuGp66TM8Or/ilaXXPWo6LGcBla8soKffPhTuOJ3lOWewfWvfZH7R9kXsJ7y/M4ErZk3yMBp1Q77gpzb/fbHi1LR6D27XXA4+xaZwfrhI8Mich+yzt1pzJwdn93jNnurGvlwfy3fWTKJh5bPIiPB3+12n5skKZR/vWwKI5NjmZE5g121u6hrq+uy7Z6jEm2OjB9JWVMZsZ5YHll+Md+/bHBPSjoZnFRBwAr0LSXTcFiqHUo6SeQeslvrlm+WfGqzLfZVVyrvcQaxT8C5L8hdwJnZZ0Ymss3Onk2aP5WFxR9LpJo2Tqag1+yWO4XEHEnLWEGZRJO/sPsPLrxUHj/4g2yfMUHuOJprpZRv4bdh7LkyCBvbSwXUhIvgn/4AZywHIDsln599/nkSYmSS3S3TbuH5Zc9HJrwdi/ykfA40lBC+8hE2qSABK8CLfiWRe6AJ3voR/PtUuXB9/v9FNWFA0h8eH499+hhPbn+yT/s7EeoD9RTXF/PGgTdoC7fxVslbWNpiZbiWtbExcjy746wvSzoqc6Ldl3Wb/D4631mdDI5zz2xXkpyYLbNytzwTvQNK6Nm/DHaGrHMvb+55ApPDyk2HUQquKur9du68iZm8ffd5XD1bamyX5i8lrMM89uljXbZ1nPt3z/ouIDMnF08ZSVbi4NSJORWkx6QT5xGn06fB1EZbca+zky5dL4/lWzq2iKs/1LE88QSc+7iUcfzXkv8iySd52hVTVvDqjLuJa2uUE1YpmfRyaAMEm6KRu0NPIlDpBSLtaoXEQacVyLR5pyHFiBlw04uw4vnu3+9gGDDzOtEG7waf6WNMUu9Re3vykvMIWkHKGssi5birw0dpbTgMT1wjg79558Ktb0Q0Yp7Z+Qx3vX0XAGErzCObH+GpHU/1eZ/Hy76jMmu4KdjEurJ1vHngTQqSC8hLHM0Ps7JpGdND1VhidvSimjlRFByt0KmL3EG+y8mXy/hIe2ZeL4HHJ0/I4HdvMguDnKHr3I9R46615qXNZZydl0Z2Uu+OVynVYZB1YupEluQt4fHtj3cRFdt9ZDcZsRksGrOIhaMWcuHYC0/yfzL4UUpFZqT2KXJvspX72ud/A00iuwoywOekZLxxEknX25NUDM8JOffOGMogbtfrMljnpEvSJ0Rrm5NyxHGD3Pr3dvvtOJqJF8l7avdKNAmSQzfMqOb5acJJQ+2v38+OIzvwGl6adYi1sX448B5c+QgsfwJGRwW+3i55m1UHV7G/bj/barZRH6inpKGEllBLv9joBEIew8PTO57mk8pPuDj/Yn4w/4ccMjT37nsm0sCkR9pH1qcq5w5w1q1SqtqZcedLnr1xaNe4wzBw7j2lZXZWNLCnspHLzsjpdv2x+OrMr9IWbuM/N/9ntBsO0gB5fMp4lFL8xwX/wU1Tbzqhzx9qOFFl3yJ327kfKY7WDZdtlLTLiOkyMFm+RRz5qLPEsTuRe05RV+d+YN3xTa0HqbjZ+YrkzJ3oK2NiNPWTlAPxmZAwItqEoifm3A7nf09qttMLZCBu7zuS5jmVDuc4yEvKA0TIbVftLhaPXUyyN4E3EhKkX+iMa7q8x9HfWX1wNesOi/iZpa1IhH2q2Xt0L7GeWJbmLeW9svfQaC4ccyGzR8zmnrPv4a2St7h/3f2R88vSFgcbOjXqaK+Zfioj954wzEjqbCjn22EIO/eKpgq8hjdSj92ZlZsOYyhYOu3EvqD85HyWFSzj8e2P84WXvsBrxa9haYu9dXtPXZ33EOLsEWczIXUC2XE95CBfuxc22U1QnEYIwebo80N2SubMm6UUbvtLkvdMzZPIve6QTKEfOaOjc7csePIa+PPS6ESW3tj2IvwkBx5bJrfX7ZUN22uROGV1t6+OarX0REIWnPdtqdl28sR73xbHM0DNkFNjUkn2J7O+Yj1VLVVMTZ/KorGLWZWSQWDmdV22bw42R8Tf3jn4DuvK1pHqlwFWR0LjVLPn6B7yk/O5OE/a9I1NGhs5d26YfAO3z7idv+/5O28flAlgT+94mmV/WybdpxxSxkp9O5zanHtvnHG9PLrOfWAobyonOy6728qNLaV1/Om9/Zw3MbPHQdS+cN+c+/jenO9haYt7//feSDf7Uz39fihwzaRreGHZCz036NjwF/jU7lPb2O7kdBz1oY/lRHXKBetKJE2SPEoGVmv3ycmbmi/le84Elpo9Mn29/hA8cbVU4Kx/rFu9dUDKFkFSJ3EZHcsT20vEOrfciSOOT/XPydOHWjqmDAaA/KT8iKJpYVohF469kEZbY74zxfXFgNx5bazayMaqjSwrWEasJ7bfnLtzlzsvZx6ZsZlcNu6yDr+fO864g7SYNFbule/sH3v/QUiHRMPewTDljssbL5PMTgeZE2UWbNHQvisfus69hwlMJTXN3PxfH5Ia5+PBq2ac1D5iPDFcW3gtjyx+BI/h4QdrZdr2ZzFy75VAk9R5O70vmyq7NkUoXS+zElPGRienpBdEb7VLP5Jo2qlicN5XZit0XvxTqN4Jz94EL31Nmjd3R8k6GL8IvrUdvrGlo+NOLwCUPVB2ghf9xJHSRg4GvEFyXnIeAUuUEielTYpUCnWnjeSkZG6edjOWtghZIebnzmdCyoR+ce51bXVUtVRRkFKAz/Tx8pUvc9uM2zps4zE8LM1fyurS1Wyu2ixdp4iKvkUYfba00zudd0nnfL3DeMVQZOg696auzl1rzdf/+gnBsOaxW84i6xgDqX0lMy6T6yZfF8nz95u2ylDFmfDhKPc1VkV7bR45IOvrS6U1m2FI3h1soSjbubfUSkeizs798EZxpmffLtP673gXsqfD/tVd7ag7JIqOY+ZLxOfrVFbojZWZnEknNg4DiP3OQOwAN0h2BlVHxo8k2Z9Mki+JvKS87p370X2YymRJ3hKyYrPwGT6KsoqYkCrOXR9P/9VuONJ6hA8Pf0hFUwVa6y5SFbGe2G7vsi/Nv5SgFeS+d+9DoUj1p0YuNo2BRlEjXfogfPHFk7Lvs8iQnKEatsJUNVd1ce6vbS3nk5KjPHjVdMZn9awNfiLcMvUWnt35LEm+JOK98af0s4c8jXZP0bY6Sak0VYoDrysVJ71vlawfI12wGDFdKjrSJ3SsI07Kiep+RCL3jbK96ZFZrCDT/N//vdwxHD0ITy2Xygenm8/YeT3besb1J1/eljZO9GYGuEGyM6jaXn10esZ01patRWvdIQWyv24/oxNH4zN9fGXGV6hsriTGE8PE1Ik8v/t5qlqqyIo78RnW33/v+6wulQtuUVYRF4yRNoDHCoSmZUxjTOIYiuuLmTNyDvGe+IjMxa/W/4p3St5h1bWrOso5uPSJIenca1prCOlQhzLIoK3YOCEr4Zh17SdCSkwKPz73x7SGWk/5Zw95GqMNo6krlZx7fFZUN7v1qEwaGjlLtilYBJuekqnfZrv0SHKu1IHHZcj7rLC0VZu1ouP+8s+Dtb+RSVI7XhY9mtUPyD59CRLZ98T59578/3fMXLFrgCplHJzJZe37FkzLmMZL+16iornj7O19dfsikf7ywuWR5c5M3l1Hdh2Xcz/ceJhVpatYPmk5Fc0VrDm0hmUFy8hLyuN3G3/HluotfZKqUEpx6bhL+f2m33Np/qWUNZWxqnQVzcFmVh1cRU1rDS2hFmI9Q7Mb0kAyJJ17dx2Ynv7oIPuqm3j0i7PxmP2TbXKiEQJNiPhS32YTDnsa2jn36t0yAJqQKc5971syDX7WCklpAEy8GO5p1wQ5Ns1Oy9gpmtQ8KZWs3i0TjnJmdtzf2Hmi6b3rNZGS9SVI9U18puRn+0OXvD1z75R2bQNUKeOQl5THbTNuizTuBpiRKemwLdVbIudH0ApS0lDC+aPP7/IZTnHAriO7ODe3h4lc3fDMrmd4dMujJHgTONR4CEtb3HHGHYxOHE2CL4GffvBTJqVOOrZUBXDtpGtpCbWwJH8J7x16T2ax7lsZaZFY3VzN6KTRvHngTYJWkKX50rCjuqWaeG+86/h7YOg796qd1B/YxC9fS2buuDQumHwaxLueug7i0uHqP/f/voYCjiYMRCcJxWeJXoxTCtmb6mBybkfnPuMaePU78NYP5fXITs7dFy/18R//SWYuXvsEvHCb7GtMN3K3pxqlRDJ4gDGUwV2z7uqwbGLqRLyGly3VW1g8djFBK0hpQykhK8S4lK5ibMn+ZHITcnmj+A1unHwj3j6mP7bXSkXLLz/+JT7Tx5yRcxidKDO8ryu8Dq11n+8E0mPTI3LOTorpz1uj51ZlSyWjk0bz6JZHKW0s5cKxF6K15qoXr2Lx2MV8b273fWs/6wzJAdWIc//gUfj9fJJWfoUfWg/xk8sm9Fyqd0oN2CyaHS5CQ4WkXUyfTO8HqQ13BkdjU3tWAIRoazVnoPOsL8PIM2DnyzKDtbuuO/kLxbGnj5cZpGfZ7e56y7d/BvCZPgrTCtlctZkHPnyAuU/M5Rcf/QKAccndK21+48xvsLVmKw98+ECXdWErzDM7n+mSjtxZu5Np6dM42naU8qZyvjDhCx3WXz/5+hOavZ2bmEucJ47SxtKIlERViwQIh5sOU9dWx/tl77O6dDW1rbWsLl3d62Bwa6iVlftWRrY5UH+AF3afHtG0gWZoOvfmcmKVh6QPHqFi3JU8FLqSK4z3KHj7JLqz9BWnBruxvGM992eNDX+BdQ/Lc6djTfIoGQCFaM4dRBe9t4gwvUDuhGwRLQwTLvs1oKKDqZ1xtNNn3SiR9MLvwOW/OT2R+yBnWsY01les54ntTzAhdQJrDolefRc5YZsleUu4edrNPLPrGV7Z90qHdesr1vOj938UaWEIkg6pbqnm0nGXctOUmxgRP4JFY3qQOz5ODGVEUkWXjZO7vermagLhQEQK5LXi13hxj1TPlDeVs79+f4+f9/zu57l3zb1sq5Uyyye3P8kP1v6AxkDjKbF3MDM0nXtTOdkWqOxp3K/u4C8x1xOa/y3Y82Z08kt/0X4a/OGeG2kPa7SGVQ/Aew/J64aKqHMPSLMTErJkwDRrqnSe742F3xaBq/Z3XblFcMXv4LzvdP+e0XNg+VMw96vyOiYJzrwpmtf/DDM7W7ocfWX6V3jq0qd4bMljPLjgwV6rvL4+6+uMjB8ZmS3qsPuoaABtqtoUWeYIlU1Km8Q3z/wmr1756jF74B4PziDv5QWX4zE8VLVUUdEk4zrx3njeKnmLdw+9G5n5uq5MpBQ2Vm7scofhrNtfJxcAZzKX0xN4ODMkz4SKxjJGtDYRzPsc7+yo4uKpI/CM/5ysPNR1dl4EreH1+6ITY06E9lPjy4eYc9+/JqpmeDJU7ZS69cZy6W/ZWC4ljcntVA3jMyUSv3NttASyJ2KSo7Xj7Zm1omfdF6Wg8JITn4w0jFk8djEv/tOL3DXrLpRSFGUXccm4S3p9j2mYTEqdFKlPd9h9pGfnXphWiFKqY7vBU8DlBZdz5YQrmZI+hYzYDKpbqiMqsFdPvJqmYBMhHeL2GbeTl5TH2rK1vHvoXW589UYe3x4VAwtaQT4q/who59zrijs8DmeGpHMvrz/IiFCQjb4iWoJhlk4bKZEeCko/7vmNDYdh3e9g60nk3BznHpc+9Jz787fKxe1k2fNm9HnZRunq40TuAP5k8A5fCeTBjlKK/OT84x5/Gp86nuK6YoLt2hw6zn3v0b002HdlO2p3kJuQS6Lv1M4lcZiVNYv759+PoQwyYzOpaq6KjLNdUXAFKf4UJqdNZkLqBOblzOOj8o/4yfvS99aRYwDYXLWZ5lAzIM68NdQa0ddxIvjhTJ8uuUqpJcBDgAk8qrV+oNN6Za+/BGgGvqS17trl+BQQtIJUBesZYSmerRxFSlwdc8algWlA1hSZxt4TTku1+j4IUPXEkWIZIBwzL5qWCTTJLMrBnBJoqpZ69HBQ7mBOZuB5z5syDb/hsAhogUTuTl49YWj3jx0uaK3RVtfBRq0BDRodfa41+bEFGEEPeyr2k5+cj2VZHKw8zKS4KZTUH2TDvs3MHjGb4rJSpiXNpKG2VQYqtXym89zZd4f9Ed2n87zjet1lW4BRreMprymnzDhCZuNofLXJ/HTivxPnjaN8fx0z9TzeOrKWIIrzUpewe88e9u4oI84Tx7pdG8lpGM/4lAKOFreyMXE3I+rGoTCo2N7IPqqwwppwyCIcErVQ02PIn9fA9ChMj4HWYIUswmFNOGhhhS0sS2N6DAxTEQ5ZaAt8sR68PoN2/x2UIZ/h8RoYHoXHa+DxmrKtv38rrtSxph0rpUxgF7AYKAU+Aq7TWm9rt80lwF2Ic58DPKS17vVefPbs2frjj3uJsnugrLGMi5+/mB+Qyf37v8vFU0fwy6vPkJUv3iWqgN8t7t55ffAIvPptGD0Xbn39uPcNwF+ulLK9iUth1c/grvXwx4ukI/uCu0/sM08H+1bDfy+T51/fhE4eSzAQJtgWJhQIE2yz5HlbmGAgTKA1RDgYxhvjwev3oMPyO/GaQdQzNxAcvwyreB3KF4+uK4UF3wLDi171M8iYiF70L2jLQmsL7N6zGgttWSit0dqK/KGJbKu1JQ7Afh0Khgm2hQi2BuWxLUSoLYRWGqU0gaYgrS0BUY9UAJZUKhoGhmliek0Mw8CyFMow8Pm9KEMTDgUIBwOEQwGwNFoZKAzQCo0CFGgFhoHp8eDxmijDRBkGKANDGfJcg2WFscJhLCuMDoewLAssjaWtqNOzos+1Zbs4S2PJwk5O2HGBWmzDdp62WrH08oVgWyuhthaU4cH0xoDWWJZGW+KA5DPbe8t2f7rjMh15ruQ4YPQQAOhuXnbcj+6wz3aPurM99j50G1rL3YJyjj1KWhdG3mdF3qs729Db6279m+7meXfL2qPEXuxjogzbWucY2etUO/s7/7VbN3HOLC6589Ju9nNslFLrtdazj7VdXyL3s4E9Wut99gc/DVwBbGu3zRXAf2u5UryvlEpRSo3UWh8+Adt7ZePrL7Ps/UKqzAzmt25nTlaATS8HUB4/HDkLXVWCfvEDdFwmaIUVVoQCAQKtzYT3tqCP3IDVmIL+88eEwxosUKaB6THtK7F8SVrLHzgnlZKT8tM5WDGpqJaxcPR2jF88iWpdhvWmSWjXesKBEOGg/RcOoy37ZNdhOel0rgMHeQAAGrJJREFUGKyQODErjLaX68iyUCTqifzENGCFsCywwiHbXvvHpJXtNDr+jqO35BoIEm6pJ9x6B2Gtsb7xBywrBDoEuhWtW9FWC+hW2p9EHWn/406G3avs7ZoBPxT/h71dLBw4COu/eiq+bpchjbL/iWNzfpPKdtpyMdd4fDF4/DGodsucx0A4QJsO4DE8WGiS/En25zm7UFhWGMNullLTWkuM6Sfel2BPcorDUCb1gQZivbG0hJqJ9cTREm4hJ2FkxC6l7M/UYnf76FsuvmF5qgDHRqubRyts2x9d3mF75zYnlNrvR78vzj0XaK+gX4pE58faJhfo4NyVUrcBtwGMGdP3lmLtKXtnA2m1LTRzkIkcpGT1m5R03ujJHx/jU0rg4CnOlx/eDTvePPZ2A4rCQGEYHjweP6bpwev14/Mn4fdn44+NxeP14PF6ML0S+Ya1ktvQtlasQADrSDG6vhKVOQ1VV4pqrpD4JacIrUzUwY9RcVmQOkbOPyXRjVJG9CQ32j1XhpxcqEhUrNpto8JBjJZGPITweRQej0TjyuPB8HhQHi+GxwRLo7SFEROLYRgEqqoINzVhpqSgPSah2lqssIWRmoyyNLqyCqO1DdOQE1tiSoVWYKHQSqJYud+w14G93lkHOhyGlhZUOIwyTEzDENsMA0wDZZhgGuJ8lCJcV0e4vg7DH4MRHwehMIRC6GAQtIXhl7EKq74ebVl4kpJRXg9WWxtWWxu6tQ18Xsz4eEzADITQpkHY55PlPj/K58PweNBtbei2VvvYANqK+sR2MWfU6So+KP+AeE88U7JnsK/pIAdq9jA/pYjD1ftobqojEOelwRPirIQpGE0thOsbIBjETE7GjPGjw2GUaWImJqE8HnGKYXF6yuvF8Megg0Gs1laMGB/KH0P46FHCNTV4UlIxkpKw6uuxAmHMpCQO6xr2Ve0lLmySQiyjvCF0SwsYBkZ8PDoYJFxfjzJNjNhYKlqraAu34TP9NIaaGJ00Ggs40HQQw+MlbCqyk3LY13iAotx0Yvzx6FAQq74B5fPhzc0FrQnV1KADAQiHCRw8SKhCqnWMhARQCh0KyfcWDstd43GSembTCZ3Bx0NfnHsf7s36tA1a60eAR0DSMn3YdxfO++pdfPo/fycnKQ+rpYVAYz2hlhZ0sA2CbVjF69D+FHR8NjoYwGo+iqkMvN4YjNo9dgSssGJHYrUFsAJtKI8XfD7noo2KjZXXIYmiVUwMmAa6tQV9tAQjZQRGfDqUbwNMlBmLbq5DJeSgwmEIhTHj4vDExaMbG7Hq6zE9HgzTgwoGIBBAtQVRwYCcZKEQRjCE0hpD6x4PpmGv1/Yh1wp0D7lz5+AqwLQsDEuf4tHzTqVkn7zc7kUZsNGeyam63lYcJ0ZSEso05ftw/oJBwr28R/l8GAkJBI8eBcsS52MYhI8cQRsG3hEjMBITI7apSM5XY0byINE/7aQyOi3H48FMSEB5vWirDcJyJ0Y4hA5b8jocFidnaXypqXhSU7Ba27AOlaM8HpTXi/L5wPBgNTWDpTHT0lHKIHT0CNSHULGxGAlJqHS/OMf6ejAMeV84jFnfYDvzNqxAgHAwiBEbixHjt9sAGvZZat+NRv46fi9ZjSaBUB3h1gPE1h1mlGHiiw8Tl5JOiXGElKDFLJ2L3/Rijs7ETEpCeb2E6+rQra3g9UAwRLixEau1RVJZpokyPei2AMG6OnHyMbFYzS1YtUcwU1LwZ4wjfOQIwUOHMBMTMRMTCdfVEddQR3ybps0XJpySiH/kBAy/X+6Cm5pQPh9mYqJE+s3NGHXx7K/ZhtLNTE6bTFLCKNpCrRw5WIoZDpHqTSLG8OMPalqrKzGNGLkYJSVitbbR9N57YJp40tNRMVKJFT93Dr6C8ehQkEBtNdtqtrO/sYTFBUuIj0mMXMCVYdjHWskyw0CZdsAS48dMSJDUWUsL/gn93xOiL869FBjd7vUo5Ow93m1OCROmTmHC1Ck9b/DYMmipgTv+IcJTP8+XWYw3/g0eGCOTXErWwpcfgVFn9m2noQAEGqHiU3jsMrjxAZlEc2i9lPwVvwt//yr8n6ei3X6OHoSX74ZLHxKZ2WOg/3w5OhDEuupxdNhCGbZjNAzY/Qbq77dL2d83NsOuV1ErvwGffximfj6as9W2M7FzvFiWOJ+4OPhFPsxcgQ60obe8gL5zg9wmBoPoQFAeQ0HY9ir6nQfRi/4/dNZ0dGszNFVj5k3FMMPoR5eizroV7+X3oOr3Yz28CGLTUd9Yj/L55M/seaBId3aOEedJ12WWxojxozzd/0x1OByJoPB6UUphtbSgQyHMlBSUYcg24TCGT+qwrUBAsp6+odv4uD95+ZPf8sctf+SDG57j7pXLyU3I5XcX/I6sYDMbtv2FJROuJDPu9A2Yb6vZxn0rrwXg60XXsWj6l3vd3t9Szb+9eScrpqxgboGMMWmtWfHUfBqDjVw76TKKZtzG9c9ewL/MuZHrCrt2reqJurY6bn7lBg7US2AzeuHsiM7NYKQvwdxHwASlVL5SygcsBzqLK78IfFEJc4G6/si394m8BVC+FZprRZa1tU6csFPV4cxsdBoyHwvLgieugt8Wwf7/lWXOzMvcM0WG1pkeX203PdAaVn4Tdr8OW5/v025U5VaMxmI8GRl4s7PwvHYbnt1/xZOWhoejmH6NabZiHtmCufM5DK/G2Pe6RGdxcZgJ8ZitZZg7/4onNRVPejqezEw8qamopkOoUBMqZxpG/lmYuh6POoo3OxvfiCz8+/9CTHYMsVOnEptYQ1xWgPixMSQsXEhi0n4St3yduPwMYnyVxKYFiFlwOWZKCkbOVDxxJp6sEZjJyRixsb06diCacjHNDlGr4fNh+P0YMTEd/k89OXZAbsX9foz4eAyfD+X1YiYl4UlLkyjK2aadIzfsC5BL94xPGU9Yh9lYuZHiuuKIHnucN47bz7j9tDp2oIM+TXfNeTqTEZvBM5c/wzLbsYP85hx55LykPDJjM4nzxB13rfu7h97lQP0BHljwAKYyIw3AByvHdO5a6xDwf4DXge3AM1rrT5VSdyil7rA3ewXYB+wB/hO4s5/sPTbjPgdoccTF0ZpX1vxKHiNt3vpYDvnRo/JZbY3wvz8XwajOUq9O67UaqQnm0xdgzxvSAHpvxxl/3dJULRU4DeVSqhgOiga6o4NeVyq6LaZfWswVrxElxN1vQLBd5/rVPxfBrc7/twp77DtrCuTYsrvORK4Nj8mxWf9Yx+VH9tvv3SoaLpufFg120y8XNRBd9BHTIS3v2P9HlyGB48xvf+N2LCzOye1FE+g0kOpPjShLtpf4Pl4ceeS85Dxx9sl5x13rvqFiA/HeeJbkLWF04uguE74GG32qc9dav4I48PbL/tDuuQb++dSadoLkzJI2bvtWSV132jgRnyrfIg5y5EypSe9LrXvtfnjzBzJLcvat8PR14tg766TEpoiWSvUuaGuAV+8RO8bMk4tDoLl3eWCn/h4ttePakr9auyt9/SFRTEwdC9vtm6YlP5PSz71vi3BWOCjOHsT5nxHV7KbSdu6ZheCJAV+iOPPxF8LqB+33vGvrp2+O/t8Bauwf8KanRWt91OyOE5SWP+k2UhhG5CXlkROfw+ik0XznrO9EpAAGCtMwSY9Jp6qla3Oe48HR1XEi+PzkfD44/AEhK9TnGbbrK9YzM2smpmEyPmV8JHK3tEVDoIFkf/IJ29cfDOJZNyeI6YG8c8XpHVgraoRTrpB1aQWyPjm3b8599c+lnvXyh2Sq+yW/jGqZdCZjouiPb31BOhEteUCcZzggEW9vVLfrGem0igPRsQmHJHJPHgUFtp583gI44zppGLz9JVl2YK10QoJo+gjkYrP/fyWV5E+Q//8lv4AD78LvzxGZ3PzzJHV1eJPopysjqqFTvVv2U7NHovqxnYS5kkZCfMYxD6XL0MBrenn9C6/z6EWPDrhjd8iIzUChyI7LPvbGPXDlhCv5/tzvk5sgstKLxyymuqWa14v7Nt/laOtR9tbt5cwsuWstSCmgpKGEtnAbT+14ioufv5imoFTAHGo8xJaqrq0OTzfDz7mDpGaOHpAOQHkLos490/6xJuUeOy3TWgef/g2mXx1Nw5z9lV6c+3hxhJ88LhHy6DniCD0xx07NVLVrUFxXGm00bQWh7qDYmpQrTS6UIUJcphcmXQI7X5EB352vyr4KLhANGYDNz8K/T5VIfvrV0X3MvA4u+jE0lMHky+Gcr8m+PnxE1uctkLRMyxForpb/t9MQobNzd3HpZzLjMkmPTe+z1nx3ZMRmcM2kayJln+ePOZ/xKeN5dMujWPrYpYwbKmXCfVF2ESDpK0tbFNcV88aBN2gKNkU0d3750S/52jtfO2FbTxXD17k75J0jvS6LboLp18iypB4i99r98OLXxKlteQ5CLVB0Y9/2mTFR8ualH4rglVLSkHnsfNjzlqQ3St7v/r3VO6N5+/rSaOQOEjE3HJYLTOYk+OY2mHaVrJtxtVyEXv4m7HpVIvCJF0Ndieiqv/wtuVv5ytuwqFNDg/l3wYrnYdlvZcauMqWrkTdOPiPQCCUfyLY5RdJsw/DCqLP7djxcXE4RNxTewD/PPLVZX0MZ3Dr9VvYc3cOqg6uOuf2Gig14DS/TMqYB0d6wn1R+wsZKkbl2nPvm6s1Ut1RT59xJDxDD07lnTBTtk9S8aNS97DfRbkDJuTJ42dYI7/5aHCSIc9vwGDxzkzxmTRXH1hfS7bpVwwMzro0uL1gkzvu3RfCni6M5bCeFAxK55xSJOqKTlvHYee0Da0GHxWaQNIhT216wSORyP3lcNG8mLZUmFgDP3SwpmX96ODoA2pnxF4pOjj9BhNeskAyQOheaPXYOP308XPwz+NJK2dbF5TQyP3c+X5j4hWNveJwsyVvCqIRR/Pe2/z7mthsqNzA9Yzp+u+dvXlIeHuXhie1PENZhDGWwrWYblc2VVDZLnwenZHKgGJ7OXSkZcFz8o+7XJ+WKw3zxLhkw3fRXWV6yTqpQ9q+W/HPRjX0X2MqwnfvEJaJl7jBjOZx5M5x3j70PO3p/56fw3C2Sx64vlZRR0ii5o6g7KJ2IPLGSUgFZ1x3n3wczV4A3Xpx7ZqHU3h8plrx81uS+2Z+3QB5zZkGq3dRh9xuSBkrNEzGwMXP79lkuLkMAj+Fh0ZhFbK3eSsgK9bhdc7CZ7TXbOTM7GiR5TS9jksZQXF9Mki+JuSPnsqN2B1urt0a2capx/rb7b7x54PTPXh+ezh1kcs+UZd2vc3p1fmpHzvvekUqRgx9J1L3gbkgY0TECPxYpY2HOV+G873ZcnpAJl/9alsckw8H3pQ6++F1Aw0tfl+0yJkl0Xlcq4wUpY6XSx2lb50TunVFKmlp8a5vI7iol0bvpg8/d03f789s595QxgIra4XHrwl2GJ4VphbSF2yJ6791R2lhKSIeYmNZxgNlJzZyTcw5T06ey9+he1lesx1QmpjIpritGa82vN/yahzY81K//j+4Yvs69NxxHmTwapn1BBiAPb5IuQmPmwgX/Ct/8FOLS+v6ZhgFLH4CRM3peP3oOHPxQSiabKuUCcthugpBZKBedoyWSmkkZDWn5cocB0QtSdygl5ZgOi38EX3pZSif7Sv7n4POPyEXRGxPtZ+qkaFxchiGT0+TO1smXd4eTZulcZ+/MCVgwagGT0ycT1mFW7lvJxNSJjEocRXF9MYcaD1HbWktxfTGHG0/vvM7PpnNPHy/pkysfkWqRQAO8b/cDHW1ronXXt/NkGX02VO2I1qp//veS9jA84siTc6XCR4clek6zGxr7EqL9RftCcq7s63gwDDjj2mhnIyc14zp3l2FMXnIeMWYM22t77lDmOPf2s2UBzs09l0mpk1g4aiGFaYUA1LbWMjVjKnlJeeyv28/mqqhA4fuHeyio6Cc+m87d44fr/yqVLPkLASXVMYkj7ZREP+FcON7/g+TQx50PM6+H3NlS2tg+r97euSflnlxzjRPBkVjIcJ27y/DFY3iYmDqx18jd6d+aGdtRemFG5gyeW/Ycyf5kRiWMItErnammZ0wnLymPkvoSNlVtIsaMIT0m/bQ7934IT4cYcWmSZy7bICmZ/nSiOUVScthcLQOtSsHlv4muby9rkDxGtoWe8+39iSMp4EbuLsOcwrRCXt3/qijAdnP+VzRXkB7Te529UorC9EI+Kv+IqelTsbRFwArw5oE3mZI+hZEJI1lXtg5LWxE5hf7msxm5d8YRExvdz9Ug/gQYIXWykQFMw5Q/6OjEk0dFI/fOWjangzHzpeome/rp37eLy2mkML2QhmADpY3diwlWNFd0Scl0x6ysWaT6UylIKYjIHFS2VDIjcwbzRs6jtrU20pP2dOA6d4DJy6Sp8/gL+39fzgUk79yu65xB04Rse1AzV9Izfa21P5XknQPf3gPx6ad/3y4up5FjDapWNleSHX9s6YPbZ9zOC1e8gMfwRITKQNI0c0ZKSnbNoTUnb3AfcdMyADkz4d4u/Zz6h3l3ykxTJ6fdHo9fomUn728Y8I2B16hwcRnOTEidgKlMttdsZ/HYxV3WVzRXMCtr1jE/x2f6yIgVnaX0mHQSvYk0BBuYkTmDEfEjKMoq4ref/JZgOMhtM27DNHqXxz5Z3Mj9dJOaB2fd2vP68RfKQKuLi8tpwW/6GZcyjm0127qsaw21UtdW16e0THscWeGs2KyImuXvL/w9l+ZfysObHubBjx48Jbb3hhu5DzY+/4djb+Pi4nJKKcoq4qW9LxG0gniN6MCpUwZ5IoqUd5xxB83B5sjrOG8cP13wU+bmzGVm5syTN/oYuJG7i4vLZ56zR5xNc6iZT6s/7bC8olnKII83cgdYOGohS/KXdFm+rGAZY5L6seTaxnXuLi4un3nOHnE2CtWlFv1kIveBxnXuLi4un3lSYlIoTCvkg8MfdFjuRO59qZYZbLjO3cXFxQWYM3IOm6o20RKK9iWubK4k3htPvDd+AC07MVzn7uL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119 | "text/plain": [ 120 | "
" 121 | ] 122 | }, 123 | "metadata": { 124 | "needs_background": "light" 125 | }, 126 | "output_type": "display_data" 127 | } 128 | ], 129 | "source": [ 130 | "# Make a plot of motion parameters \n", 131 | "plt.plot(motion)\n", 132 | "plt.legend(parameter_names)" 133 | ] 134 | }, 135 | { 136 | "cell_type": "markdown", 137 | "metadata": {}, 138 | "source": [ 139 | "## Segment" 140 | ] 141 | }, 142 | { 143 | "cell_type": "code", 144 | "execution_count": null, 145 | "metadata": {}, 146 | "outputs": [], 147 | "source": [ 148 | "# Segment data in SPM\n", 149 | "\n", 150 | "\n", 151 | "# Plot segmented gray matter, white matter, and CSF\n", 152 | "\n" 153 | ] 154 | } 155 | ], 156 | "metadata": { 157 | "kernelspec": { 158 | "display_name": "Python 3", 159 | "language": "python", 160 | "name": "python3" 161 | }, 162 | "language_info": { 163 | "codemirror_mode": { 164 | "name": "ipython", 165 | "version": 3 166 | }, 167 | "file_extension": ".py", 168 | "mimetype": "text/x-python", 169 | "name": "python", 170 | "nbconvert_exporter": "python", 171 | "pygments_lexer": "ipython3", 172 | "version": "3.7.3" 173 | } 174 | }, 175 | "nbformat": 4, 176 | "nbformat_minor": 2 177 | } 178 | -------------------------------------------------------------------------------- /03-fmri_data_preprocessing/images/harry_potter_400x400.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fMRIAnalysisCourse/fmri-analysis-course/5495887620b54bc3f6b5c83ed9fa35df149414c7/03-fmri_data_preprocessing/images/harry_potter_400x400.jpg -------------------------------------------------------------------------------- /03-fmri_data_preprocessing/images/screenshot_solution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fMRIAnalysisCourse/fmri-analysis-course/5495887620b54bc3f6b5c83ed9fa35df149414c7/03-fmri_data_preprocessing/images/screenshot_solution.png -------------------------------------------------------------------------------- /03-fmri_data_preprocessing/pyscript_realign.m: -------------------------------------------------------------------------------- 1 | fprintf(1,'Executing %s at %s:\n',mfilename(),datestr(now)); 2 | ver, 3 | try, 4 | %% Generated by nipype.interfaces.spm 5 | if isempty(which('spm')), 6 | throw(MException('SPMCheck:NotFound', 'SPM not in matlab path')); 7 | end 8 | [name, version] = spm('ver'); 9 | fprintf('SPM version: %s Release: %s\n',name, version); 10 | fprintf('SPM path: %s\n', which('spm')); 11 | spm('Defaults','fMRI'); 12 | 13 | if strcmp(name, 'SPM8') || strcmp(name(1:5), 'SPM12'), 14 | spm_jobman('initcfg'); 15 | spm_get_defaults('cmdline', 1); 16 | end 17 | 18 | jobs{1}.spm.spatial.realign.estwrite.data = {... 19 | {... 20 | '/media/finc/Elements/ds000003-00001/sub-01/func/sub-01_task-rhymejudgment_bold.nii,1';... 21 | '/media/finc/Elements/ds000003-00001/sub-01/func/sub-01_task-rhymejudgment_bold.nii,2';... 22 | '/media/finc/Elements/ds000003-00001/sub-01/func/sub-01_task-rhymejudgment_bold.nii,3';... 23 | '/media/finc/Elements/ds000003-00001/sub-01/func/sub-01_task-rhymejudgment_bold.nii,4';... 24 | '/media/finc/Elements/ds000003-00001/sub-01/func/sub-01_task-rhymejudgment_bold.nii,5';... 25 | '/media/finc/Elements/ds000003-00001/sub-01/func/sub-01_task-rhymejudgment_bold.nii,6';... 26 | '/media/finc/Elements/ds000003-00001/sub-01/func/sub-01_task-rhymejudgment_bold.nii,7';... 27 | '/media/finc/Elements/ds000003-00001/sub-01/func/sub-01_task-rhymejudgment_bold.nii,8';... 28 | '/media/finc/Elements/ds000003-00001/sub-01/func/sub-01_task-rhymejudgment_bold.nii,9';... 29 | '/media/finc/Elements/ds000003-00001/sub-01/func/sub-01_task-rhymejudgment_bold.nii,10';... 30 | '/media/finc/Elements/ds000003-00001/sub-01/func/sub-01_task-rhymejudgment_bold.nii,11';... 31 | '/media/finc/Elements/ds000003-00001/sub-01/func/sub-01_task-rhymejudgment_bold.nii,12';... 32 | '/media/finc/Elements/ds000003-00001/sub-01/func/sub-01_task-rhymejudgment_bold.nii,13';... 33 | '/media/finc/Elements/ds000003-00001/sub-01/func/sub-01_task-rhymejudgment_bold.nii,14';... 34 | '/media/finc/Elements/ds000003-00001/sub-01/func/sub-01_task-rhymejudgment_bold.nii,15';... 35 | '/media/finc/Elements/ds000003-00001/sub-01/func/sub-01_task-rhymejudgment_bold.nii,16';... 36 | '/media/finc/Elements/ds000003-00001/sub-01/func/sub-01_task-rhymejudgment_bold.nii,17';... 37 | '/media/finc/Elements/ds000003-00001/sub-01/func/sub-01_task-rhymejudgment_bold.nii,18';... 38 | '/media/finc/Elements/ds000003-00001/sub-01/func/sub-01_task-rhymejudgment_bold.nii,19';... 39 | '/media/finc/Elements/ds000003-00001/sub-01/func/sub-01_task-rhymejudgment_bold.nii,20';... 40 | '/media/finc/Elements/ds000003-00001/sub-01/func/sub-01_task-rhymejudgment_bold.nii,21';... 41 | 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jobs{1}.spm.spatial.realign.estwrite.roptions.which(1) = 2; 184 | jobs{1}.spm.spatial.realign.estwrite.roptions.which(2) = 1; 185 | jobs{1}.spm.spatial.realign.estwrite.roptions.prefix = 'r'; 186 | 187 | spm_jobman('run', jobs); 188 | 189 | 190 | ,catch ME, 191 | fprintf(2,'MATLAB code threw an exception:\n'); 192 | fprintf(2,'%s\n',ME.message); 193 | if length(ME.stack) ~= 0, fprintf(2,'File:%s\nName:%s\nLine:%d\n',ME.stack.file,ME.stack.name,ME.stack.line);, end; 194 | end; -------------------------------------------------------------------------------- /04-general_linear_model/04-fMRIDA_slides.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fMRIAnalysisCourse/fmri-analysis-course/5495887620b54bc3f6b5c83ed9fa35df149414c7/04-general_linear_model/04-fMRIDA_slides.pdf -------------------------------------------------------------------------------- /04-general_linear_model/05-fMRIDA_slides.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fMRIAnalysisCourse/fmri-analysis-course/5495887620b54bc3f6b5c83ed9fa35df149414c7/04-general_linear_model/05-fMRIDA_slides.pdf -------------------------------------------------------------------------------- /04-general_linear_model/glm_on_fMRI_data_template.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# General linear model on fMRI data\n", 8 | "\n" 9 | ] 10 | }, 11 | { 12 | "cell_type": "markdown", 13 | "metadata": {}, 14 | "source": [ 15 | "## First level analysis\n", 16 | "\n", 17 | "Analysis performed on a level of single subject (individual)." 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": 24, 23 | "metadata": {}, 24 | "outputs": [], 25 | "source": [ 26 | "# Import libraries\n", 27 | "import pandas as pd\n", 28 | "import numpy as np\n", 29 | "import matplotlib.pyplot as plt\n", 30 | "%matplotlib inline\n", 31 | "from nilearn.glm.first_level import FirstLevelModel\n", 32 | "from nilearn.plotting import plot_stat_map, plot_img, show, plot_design_matrix, plot_contrast_matrix\n", 33 | "from nilearn.glm.first_level import make_first_level_design_matrix\n", 34 | "from nilearn.glm.first_level import FirstLevelModel\n", 35 | "from nilearn.reporting import get_clusters_table" 36 | ] 37 | }, 38 | { 39 | "cell_type": "markdown", 40 | "metadata": {}, 41 | "source": [ 42 | "## Load data" 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": 16, 48 | "metadata": {}, 49 | "outputs": [], 50 | "source": [ 51 | "# Load a table with events \n", 52 | "events_path = \"../fMRI_BIDS_rhymejudgment/sub-01/func/sub-01_task-rhymejudgment_events.tsv\"\n", 53 | "# events = pd.read_csv(events_path, sep=\"\\t\")" 54 | ] 55 | }, 56 | { 57 | "cell_type": "code", 58 | "execution_count": 11, 59 | "metadata": {}, 60 | "outputs": [], 61 | "source": [ 62 | "# Load preprocessed fMRI images\n", 63 | "fmri_img = \"../fMRI_BIDS_rhymejudgment/derivatives/fmriprep/sub-01/func/sub-01_task-rhymejudgment_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz\"" 64 | ] 65 | }, 66 | { 67 | "cell_type": "code", 68 | "execution_count": 12, 69 | "metadata": {}, 70 | "outputs": [], 71 | "source": [ 72 | "# Load confounds and select six motion parameters\n", 73 | "confounds_path = \"../fMRI_BIDS_rhymejudgment/derivatives/fmriprep/sub-01/func/sub-01_task-rhymejudgment_desc-confounds_regressors.tsv\"\n" 74 | ] 75 | }, 76 | { 77 | "cell_type": "markdown", 78 | "metadata": {}, 79 | "source": [ 80 | "## Run first level GLM" 81 | ] 82 | }, 83 | { 84 | "cell_type": "code", 85 | "execution_count": 18, 86 | "metadata": {}, 87 | "outputs": [], 88 | "source": [ 89 | "# Create design matrix\n", 90 | "\n", 91 | "t_r = 2 # repetition time (TR)\n", 92 | "from nilearn.glm.first_level import make_first_level_design_matrix\n", 93 | "#design_matrix = make_first_level_design_matrix(t_r, events,\n", 94 | "# drift_model='polynomial', drift_order=3)\n", 95 | "\n", 96 | "\n", 97 | "# plot_design_matrix" 98 | ] 99 | }, 100 | { 101 | "cell_type": "code", 102 | "execution_count": 13, 103 | "metadata": {}, 104 | "outputs": [], 105 | "source": [ 106 | "# Define model\n", 107 | "first_level_model = FirstLevelModel(t_r, \n", 108 | " hrf_model='spm', \n", 109 | " high_pass=.01,\n", 110 | " smoothing_fwhm=6)\n", 111 | "# Fit model to data (fit(fmri_img, design_matrices=design_matrix))\n" 112 | ] 113 | }, 114 | { 115 | "cell_type": "code", 116 | "execution_count": 33, 117 | "metadata": {}, 118 | "outputs": [], 119 | "source": [ 120 | "# Print timeseries of task-HRF data using plt.plot\n" 121 | ] 122 | }, 123 | { 124 | "cell_type": "markdown", 125 | "metadata": {}, 126 | "source": [ 127 | "## Create contrasts" 128 | ] 129 | }, 130 | { 131 | "cell_type": "code", 132 | "execution_count": 34, 133 | "metadata": {}, 134 | "outputs": [], 135 | "source": [ 136 | "# Create conditions\n", 137 | "conditions = {'pseudoword': np.array([1, 0, 0, 0, 0, 0, 0, 0, 0]), \n", 138 | " 'word': np.array([0, 1, 0, 0, 0, 0, 0, 0, 0])}" 139 | ] 140 | }, 141 | { 142 | "cell_type": "code", 143 | "execution_count": 43, 144 | "metadata": {}, 145 | "outputs": [], 146 | "source": [ 147 | "# Create contrasts (word > pseudoword, main word effect)\n", 148 | "\n", 149 | "# Plot contrast matrix with plot_contrast_matrix\n" 150 | ] 151 | }, 152 | { 153 | "cell_type": "code", 154 | "execution_count": 21, 155 | "metadata": {}, 156 | "outputs": [], 157 | "source": [ 158 | "# Calculate statistic test for selected contrast (brain activity for word processing), use compute_contrasts\n", 159 | "z_map = first_level_model.compute_contrast(word_effect,\n", 160 | " output_type='z_score')\n", 161 | "\n", 162 | "# Plot results using plot_stat_map\n" 163 | ] 164 | }, 165 | { 166 | "cell_type": "code", 167 | "execution_count": null, 168 | "metadata": {}, 169 | "outputs": [], 170 | "source": [ 171 | "# Plot results on a glass brain\n" 172 | ] 173 | }, 174 | { 175 | "cell_type": "code", 176 | "execution_count": 46, 177 | "metadata": {}, 178 | "outputs": [], 179 | "source": [ 180 | "# Plot results on interactive plot\n", 181 | "\n" 182 | ] 183 | }, 184 | { 185 | "cell_type": "markdown", 186 | "metadata": {}, 187 | "source": [ 188 | "## Thresholding" 189 | ] 190 | }, 191 | { 192 | "cell_type": "code", 193 | "execution_count": 47, 194 | "metadata": {}, 195 | "outputs": [ 196 | { 197 | "name": "stdout", 198 | "output_type": "stream", 199 | "text": [ 200 | "Corrected p<0.05 threshold: 5.179\n" 201 | ] 202 | } 203 | ], 204 | "source": [ 205 | "# Calculate threshold to correct for multiple comparisons\n", 206 | "_, threshold = map_threshold(z_map, alpha=.05, height_control='bonferroni')\n", 207 | "print('Corrected p<0.05 threshold: %.3f' % threshold)\n", 208 | "\n", 209 | "\n", 210 | "# Threshold images with calculated threshold\n" 211 | ] 212 | }, 213 | { 214 | "cell_type": "code", 215 | "execution_count": 48, 216 | "metadata": {}, 217 | "outputs": [], 218 | "source": [ 219 | "# Get cluster table using get_clusters_table\n" 220 | ] 221 | }, 222 | { 223 | "cell_type": "markdown", 224 | "metadata": {}, 225 | "source": [ 226 | "# Repeat GLM analysis with design matrix extended by 6 motion parameters" 227 | ] 228 | }, 229 | { 230 | "cell_type": "code", 231 | "execution_count": 49, 232 | "metadata": {}, 233 | "outputs": [], 234 | "source": [ 235 | "# Repeat everything with motion as nuisance regressors.\n", 236 | "\n", 237 | "# Print head of the dataframe\n" 238 | ] 239 | }, 240 | { 241 | "cell_type": "code", 242 | "execution_count": 37, 243 | "metadata": {}, 244 | "outputs": [], 245 | "source": [ 246 | "# Create conditions \n" 247 | ] 248 | }, 249 | { 250 | "cell_type": "code", 251 | "execution_count": 50, 252 | "metadata": {}, 253 | "outputs": [], 254 | "source": [ 255 | "# Create contrasts\n", 256 | "\n", 257 | "# Plot contrast matrix\n" 258 | ] 259 | }, 260 | { 261 | "cell_type": "code", 262 | "execution_count": 51, 263 | "metadata": {}, 264 | "outputs": [], 265 | "source": [ 266 | "# Calculate statistic test for selected contrast (brain activity for word processing)\n" 267 | ] 268 | }, 269 | { 270 | "cell_type": "code", 271 | "execution_count": 22, 272 | "metadata": {}, 273 | "outputs": [], 274 | "source": [ 275 | "# Threshold z-maps to correct for multiple comparisons\n" 276 | ] 277 | }, 278 | { 279 | "cell_type": "code", 280 | "execution_count": 23, 281 | "metadata": {}, 282 | "outputs": [], 283 | "source": [ 284 | "# Run contrast for motion effect (which voxels are associated with motion (translation in Y direction)?)\n" 285 | ] 286 | } 287 | ], 288 | "metadata": { 289 | "kernelspec": { 290 | "display_name": "Python 3", 291 | "language": "python", 292 | "name": "python3" 293 | }, 294 | "language_info": { 295 | "codemirror_mode": { 296 | "name": "ipython", 297 | "version": 3 298 | }, 299 | "file_extension": ".py", 300 | "mimetype": "text/x-python", 301 | "name": "python", 302 | "nbconvert_exporter": "python", 303 | "pygments_lexer": "ipython3", 304 | "version": "3.8.3" 305 | }, 306 | "widgets": { 307 | "application/vnd.jupyter.widget-state+json": { 308 | "state": {}, 309 | "version_major": 2, 310 | "version_minor": 0 311 | } 312 | } 313 | }, 314 | "nbformat": 4, 315 | "nbformat_minor": 4 316 | } 317 | -------------------------------------------------------------------------------- /05-functional_connectivity/06-fMRIDA_slides.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fMRIAnalysisCourse/fmri-analysis-course/5495887620b54bc3f6b5c83ed9fa35df149414c7/05-functional_connectivity/06-fMRIDA_slides.pdf -------------------------------------------------------------------------------- /05-functional_connectivity/brain_parcellations_template.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Plotting brain parcellations" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "## Load libraries" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 2, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "from nilearn import datasets\n", 24 | "from nilearn import plotting\n", 25 | "import numpy as np" 26 | ] 27 | }, 28 | { 29 | "cell_type": "markdown", 30 | "metadata": {}, 31 | "source": [ 32 | "## Plot brain atlas from Nifti file" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": 3, 38 | "metadata": {}, 39 | "outputs": [], 40 | "source": [ 41 | "# Load atlas\n", 42 | "ho_atlas = datasets.fetch_atlas_harvard_oxford('cort-maxprob-thr25-2mm')\n", 43 | "ho_maps = ho_atlas.maps\n", 44 | "\n", 45 | "# Plot atlas using plot_ROI funtion\n" 46 | ] 47 | }, 48 | { 49 | "cell_type": "markdown", 50 | "metadata": {}, 51 | "source": [ 52 | "### Excercise" 53 | ] 54 | }, 55 | { 56 | "cell_type": "markdown", 57 | "metadata": {}, 58 | "source": [ 59 | "Select one of atlases from [Nilearn database](https://nilearn.github.io/modules/reference.html#module-nilearn.datasets), and plot it on the brain." 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": null, 65 | "metadata": {}, 66 | "outputs": [], 67 | "source": [] 68 | }, 69 | { 70 | "cell_type": "markdown", 71 | "metadata": {}, 72 | "source": [ 73 | "## Plot brain atlas created based on MNI coordinates" 74 | ] 75 | }, 76 | { 77 | "cell_type": "code", 78 | "execution_count": 4, 79 | "metadata": {}, 80 | "outputs": [ 81 | { 82 | "name": "stdout", 83 | "output_type": "stream", 84 | "text": [ 85 | "[[-25 -98 -12]\n", 86 | " [ 27 -97 -13]\n", 87 | " [ 24 32 -18]\n", 88 | " [-56 -45 -24]\n", 89 | " [ 8 41 -24]]\n" 90 | ] 91 | } 92 | ], 93 | "source": [ 94 | "# Load atlas\n", 95 | "power = datasets.fetch_coords_power_2011()\n", 96 | "\n", 97 | "# Select coordinates\n", 98 | "coords = np.vstack((power.rois['x'], power.rois['y'], power.rois['z'])).T\n", 99 | "\n", 100 | "# Print coordinates\n", 101 | "print(coords[:5])" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": 7, 107 | "metadata": {}, 108 | "outputs": [ 109 | { 110 | "data": { 111 | "image/png": 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\n", 112 | "text/plain": [ 113 | "
" 114 | ] 115 | }, 116 | "metadata": {}, 117 | "output_type": "display_data" 118 | } 119 | ], 120 | "source": [ 121 | "# Plot coordinates on brain slices using plot_roi function & .add_markers method\n", 122 | "display = plotting.plot_roi(None)\n" 123 | ] 124 | }, 125 | { 126 | "cell_type": "code", 127 | "execution_count": 10, 128 | "metadata": {}, 129 | "outputs": [], 130 | "source": [ 131 | "# Plot on glass brain using plot_connectome\n", 132 | "n_rois = len(coords)\n", 133 | "connectivity_matrix = np.zeros((n_rois, n_rois))\n", 134 | "\n", 135 | "# Plot using plot_connectome function\n" 136 | ] 137 | } 138 | ], 139 | "metadata": { 140 | "kernelspec": { 141 | "display_name": "Python 3", 142 | "language": "python", 143 | "name": "python3" 144 | }, 145 | "language_info": { 146 | "codemirror_mode": { 147 | "name": "ipython", 148 | "version": 3 149 | }, 150 | "file_extension": ".py", 151 | "mimetype": "text/x-python", 152 | "name": "python", 153 | "nbconvert_exporter": "python", 154 | "pygments_lexer": "ipython3", 155 | "version": "3.8.3" 156 | }, 157 | "widgets": { 158 | "application/vnd.jupyter.widget-state+json": { 159 | "state": {}, 160 | "version_major": 2, 161 | "version_minor": 0 162 | } 163 | } 164 | }, 165 | "nbformat": 4, 166 | "nbformat_minor": 4 167 | } 168 | -------------------------------------------------------------------------------- /05-functional_connectivity/seed-voxel_connectivity_template.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Seed-voxel functional connectivity" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "## Load libraries" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 1, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "import numpy as np\n", 24 | "from nilearn import datasets\n", 25 | "from nilearn import input_data\n", 26 | "from nilearn.connectome import ConnectivityMeasure\n", 27 | "from nilearn import plotting" 28 | ] 29 | }, 30 | { 31 | "cell_type": "markdown", 32 | "metadata": {}, 33 | "source": [ 34 | "## Load data" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 2, 40 | "metadata": {}, 41 | "outputs": [], 42 | "source": [ 43 | "# Load fMRI data\n", 44 | "dataset = datasets.fetch_development_fmri(n_subjects=1)\n", 45 | "func_filename = dataset.func[0]\n", 46 | "confounds_filename = dataset.confounds[0]" 47 | ] 48 | }, 49 | { 50 | "cell_type": "markdown", 51 | "metadata": {}, 52 | "source": [ 53 | "## Create a seed" 54 | ] 55 | }, 56 | { 57 | "cell_type": "code", 58 | "execution_count": 3, 59 | "metadata": {}, 60 | "outputs": [], 61 | "source": [ 62 | "pcc_coords = [(0, -52, 18)]\n", 63 | "seed_masker = input_data.NiftiSpheresMasker(\n", 64 | " pcc_coords, radius=8,\n", 65 | " detrend=True, standardize=True)" 66 | ] 67 | }, 68 | { 69 | "cell_type": "markdown", 70 | "metadata": {}, 71 | "source": [ 72 | "## Extract timeseries from a seed" 73 | ] 74 | }, 75 | { 76 | "cell_type": "code", 77 | "execution_count": 4, 78 | "metadata": {}, 79 | "outputs": [], 80 | "source": [ 81 | "seed_timeseries = seed_masker.fit_transform(func_filename,\n", 82 | " confounds=confounds_filename)\n", 83 | "seed_timeseries = np.squeeze(seed_timeseries)" 84 | ] 85 | }, 86 | { 87 | "cell_type": "markdown", 88 | "metadata": {}, 89 | "source": [ 90 | "## Estract timeseries from the whole brain" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 5, 96 | "metadata": {}, 97 | "outputs": [], 98 | "source": [ 99 | "brain_masker = input_data.NiftiMasker(\n", 100 | " smoothing_fwhm=8,\n", 101 | " detrend=True, standardize=True,\n", 102 | " memory='nilearn_cache', memory_level=1, verbose=0)\n", 103 | "\n", 104 | "brain_timeseries = brain_masker.fit_transform(func_filename,\n", 105 | " confounds=confounds_filename)" 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": 1, 111 | "metadata": {}, 112 | "outputs": [], 113 | "source": [ 114 | "# Print shape of seed time-series\n", 115 | "\n", 116 | "\n", 117 | "# Print shape of brain time-series\n" 118 | ] 119 | }, 120 | { 121 | "cell_type": "markdown", 122 | "metadata": {}, 123 | "source": [ 124 | "## Calculate seed to voxel correlations" 125 | ] 126 | }, 127 | { 128 | "cell_type": "code", 129 | "execution_count": 7, 130 | "metadata": {}, 131 | "outputs": [], 132 | "source": [ 133 | "# Calculate Pearson product-moment correlation coefficient between seed and all voxel trime-series\n", 134 | "from scipy import stats\n", 135 | "\n", 136 | "corr_map = np.zeros(brain_timeseries.shape[1])\n", 137 | "\n", 138 | "for i in range(brain_timeseries.shape[1]):\n", 139 | " corr_map[i] = stats.pearsonr(seed_timeseries, brain_timeseries[:,i])[0]" 140 | ] 141 | }, 142 | { 143 | "cell_type": "markdown", 144 | "metadata": {}, 145 | "source": [ 146 | "## Plot correlation using statistical map plot" 147 | ] 148 | }, 149 | { 150 | "cell_type": "code", 151 | "execution_count": 2, 152 | "metadata": {}, 153 | "outputs": [], 154 | "source": [ 155 | "seed_to_voxel_correlations_img = brain_masker.inverse_transform(corr_map.T)\n", 156 | "\n", 157 | "# Plot correlation map using plot_stat_map\n", 158 | "\n", 159 | "\n", 160 | "# Add seed ROI marker with display.add_markers()\n" 161 | ] 162 | } 163 | ], 164 | "metadata": { 165 | "kernelspec": { 166 | "display_name": "Python 3", 167 | "language": "python", 168 | "name": "python3" 169 | }, 170 | "language_info": { 171 | "codemirror_mode": { 172 | "name": "ipython", 173 | "version": 3 174 | }, 175 | "file_extension": ".py", 176 | "mimetype": "text/x-python", 177 | "name": "python", 178 | "nbconvert_exporter": "python", 179 | "pygments_lexer": "ipython3", 180 | "version": "3.8.3" 181 | }, 182 | "widgets": { 183 | "application/vnd.jupyter.widget-state+json": { 184 | "state": {}, 185 | "version_major": 2, 186 | "version_minor": 0 187 | } 188 | } 189 | }, 190 | "nbformat": 4, 191 | "nbformat_minor": 4 192 | } 193 | -------------------------------------------------------------------------------- /06-machine_learning/07-fMRIDA_slides.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fMRIAnalysisCourse/fmri-analysis-course/5495887620b54bc3f6b5c83ed9fa35df149414c7/06-machine_learning/07-fMRIDA_slides.pdf -------------------------------------------------------------------------------- /06-machine_learning/machine_learning_fMRI_template.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Some machine learning examples" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "## Load libraries" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 4, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "import numpy as np\n", 24 | "from nilearn import datasets\n", 25 | "from nilearn import input_data\n", 26 | "from nilearn import plotting" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "metadata": {}, 32 | "source": [ 33 | "## Load datasets" 34 | ] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "execution_count": 5, 39 | "metadata": {}, 40 | "outputs": [], 41 | "source": [ 42 | "# Load fMRI data\n", 43 | "dataset = datasets.fetch_development_fmri(n_subjects=1)\n", 44 | "func_filename = dataset.func[0]\n", 45 | "confounds_filename = dataset.confounds[0]\n", 46 | "\n", 47 | "# Load atlas data\n", 48 | "power = datasets.fetch_coords_power_2011()" 49 | ] 50 | }, 51 | { 52 | "cell_type": "markdown", 53 | "metadata": {}, 54 | "source": [ 55 | "## Extract signals from parcellation" 56 | ] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": 6, 61 | "metadata": {}, 62 | "outputs": [ 63 | { 64 | "data": { 65 | "text/plain": [ 66 | "(168, 264)" 67 | ] 68 | }, 69 | "execution_count": 6, 70 | "metadata": {}, 71 | "output_type": "execute_result" 72 | } 73 | ], 74 | "source": [ 75 | "# Extract coordinates\n", 76 | "coords = np.vstack((power.rois['x'], power.rois['y'], power.rois['z'])).T\n", 77 | "\n", 78 | "# Create masker object (spheres with 5 radius)\n", 79 | "spheres_masker = input_data.NiftiSpheresMasker(\n", 80 | " seeds=coords, radius=5, detrend=True, standardize=True)\n", 81 | "\n", 82 | "\n", 83 | "# Extract timeseries from ROIs\n", 84 | "timeseries = spheres_masker.fit_transform(func_filename)\n", 85 | "\n", 86 | "# Plot shape of timeseries\n", 87 | "timeseries.shape" 88 | ] 89 | }, 90 | { 91 | "cell_type": "markdown", 92 | "metadata": {}, 93 | "source": [ 94 | "## Run k-means clustering on timeseries\n", 95 | "\n", 96 | "The `KMeans` algorithm clusters data by trying to separate samples in $n$ groups of equal variance, minimizing a criterion known as the inertia or within-cluster sum-of-squares. This algorithm requires the number of clusters to be specified. It scales well to large number of samples and has been used across a large range of application areas in many different fields ([more](https://scikit-learn.org/stable/modules/clustering.html#k-means)).\n" 97 | ] 98 | }, 99 | { 100 | "cell_type": "code", 101 | "execution_count": 7, 102 | "metadata": {}, 103 | "outputs": [ 104 | { 105 | "data": { 106 | "text/plain": [ 107 | "KMeans(n_clusters=5)" 108 | ] 109 | }, 110 | "execution_count": 7, 111 | "metadata": {}, 112 | "output_type": "execute_result" 113 | } 114 | ], 115 | "source": [ 116 | "from sklearn.cluster import KMeans\n", 117 | "\n", 118 | "# Creare KMeans object and specify n_cluster as 5\n", 119 | "clustering = KMeans(n_clusters=5)\n", 120 | "\n", 121 | "# Cluster timeseries using .fit method\n", 122 | "clustering.fit(timeseries)" 123 | ] 124 | }, 125 | { 126 | "cell_type": "code", 127 | "execution_count": 10, 128 | "metadata": {}, 129 | "outputs": [ 130 | { 131 | "data": { 132 | "text/plain": [ 133 | "array([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 2, 2, 2, 2, 4, 2,\n", 134 | " 4, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 4, 2, 2, 4, 4, 4, 2, 2, 2, 2, 4,\n", 135 | " 4, 4, 2, 2, 0, 2, 4, 4, 4, 2, 0, 2, 0, 2, 0, 2, 0, 4, 4, 4, 4, 2,\n", 136 | " 1, 1, 1, 2, 0, 0, 0, 0, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 4, 4, 2, 2,\n", 137 | " 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 4, 4, 2, 0, 0, 0, 0, 0,\n", 138 | " 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 1, 1, 1, 2, 2, 2,\n", 139 | " 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 3, 3, 0, 2, 2, 2, 3, 2, 2, 2,\n", 140 | " 3, 2, 2, 2, 0, 0, 0, 2, 2, 1, 1, 1, 3, 3], dtype=int32)" 141 | ] 142 | }, 143 | "execution_count": 10, 144 | "metadata": {}, 145 | "output_type": "execute_result" 146 | } 147 | ], 148 | "source": [ 149 | "# Print clustering labels & shape\n", 150 | "clustering.labels_" 151 | ] 152 | }, 153 | { 154 | "cell_type": "code", 155 | "execution_count": 11, 156 | "metadata": {}, 157 | "outputs": [ 158 | { 159 | "data": { 160 | "text/plain": [ 161 | "KMeans(n_clusters=5)" 162 | ] 163 | }, 164 | "execution_count": 11, 165 | "metadata": {}, 166 | "output_type": "execute_result" 167 | } 168 | ], 169 | "source": [ 170 | "# Cluster brain regions\n", 171 | "clustering.fit(timeseries.T)" 172 | ] 173 | }, 174 | { 175 | "cell_type": "code", 176 | "execution_count": 12, 177 | "metadata": {}, 178 | "outputs": [ 179 | { 180 | "data": { 181 | "text/plain": [ 182 | "array([2, 2, 3, 0, 2, 3, 3, 1, 2, 2, 0, 4, 1, 3, 4, 4, 4, 2, 1, 2, 1, 4,\n", 183 | " 4, 4, 3, 1, 4, 4, 4, 1, 0, 1, 1, 0, 0, 4, 2, 0, 4, 0, 4, 3, 4, 4,\n", 184 | " 3, 3, 4, 3, 2, 4, 4, 3, 2, 3, 3, 3, 2, 3, 3, 3, 0, 3, 4, 2, 3, 2,\n", 185 | " 3, 3, 3, 4, 3, 1, 0, 1, 1, 0, 4, 2, 0, 0, 2, 2, 0, 0, 3, 0, 0, 0,\n", 186 | " 0, 1, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 2, 2, 0, 3, 3,\n", 187 | " 0, 0, 3, 0, 0, 0, 4, 0, 1, 2, 4, 3, 2, 4, 1, 2, 3, 2, 0, 0, 0, 2,\n", 188 | " 0, 0, 0, 0, 0, 2, 2, 1, 2, 2, 4, 1, 4, 4, 1, 4, 1, 1, 4, 4, 1, 1,\n", 189 | " 1, 3, 1, 1, 3, 4, 1, 1, 4, 1, 2, 4, 4, 1, 1, 4, 1, 1, 1, 0, 3, 3,\n", 190 | " 0, 0, 1, 3, 3, 2, 2, 3, 3, 1, 1, 3, 3, 4, 1, 0, 4, 0, 0, 0, 3, 0,\n", 191 | " 1, 3, 0, 0, 3, 3, 4, 3, 3, 3, 3, 3, 3, 4, 3, 3, 4, 3, 3, 3, 3, 0,\n", 192 | " 0, 2, 4, 1, 4, 2, 2, 2, 4, 3, 3, 4, 4, 3, 1, 1, 3, 1, 1, 3, 3, 2,\n", 193 | " 2, 2, 4, 2, 1, 2, 2, 0, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4],\n", 194 | " dtype=int32)" 195 | ] 196 | }, 197 | "execution_count": 12, 198 | "metadata": {}, 199 | "output_type": "execute_result" 200 | } 201 | ], 202 | "source": [ 203 | "# Print clustering labels & shape\n", 204 | "clustering.labels_" 205 | ] 206 | }, 207 | { 208 | "cell_type": "markdown", 209 | "metadata": {}, 210 | "source": [ 211 | "## Run PCA on timeseries\n", 212 | "\n", 213 | "PCA - linear dimensionality reduction using Singular Value Decomposition of the data to project it to a lower dimensional space." 214 | ] 215 | }, 216 | { 217 | "cell_type": "code", 218 | "execution_count": 23, 219 | "metadata": {}, 220 | "outputs": [ 221 | { 222 | "data": { 223 | "text/plain": [ 224 | "PCA(copy=True, iterated_power='auto', n_components=10, random_state=None,\n", 225 | " svd_solver='auto', tol=0.0, whiten=False)" 226 | ] 227 | }, 228 | "execution_count": 23, 229 | "metadata": {}, 230 | "output_type": "execute_result" 231 | } 232 | ], 233 | "source": [ 234 | "from sklearn.decomposition import PCA \n", 235 | "\n", 236 | "decomposition = PCA(n_components=10)\n", 237 | "decomposition.fit(timeseries)\n", 238 | "\n", 239 | "# Print shape of components\n", 240 | "\n", 241 | "# Print variance eplained by each component" 242 | ] 243 | } 244 | ], 245 | "metadata": { 246 | "kernelspec": { 247 | "display_name": "Python 3", 248 | "language": "python", 249 | "name": "python3" 250 | }, 251 | "language_info": { 252 | "codemirror_mode": { 253 | "name": "ipython", 254 | "version": 3 255 | }, 256 | "file_extension": ".py", 257 | "mimetype": "text/x-python", 258 | "name": "python", 259 | "nbconvert_exporter": "python", 260 | "pygments_lexer": "ipython3", 261 | "version": "3.8.3" 262 | }, 263 | "widgets": { 264 | "application/vnd.jupyter.widget-state+json": { 265 | "state": {}, 266 | "version_major": 2, 267 | "version_minor": 0 268 | } 269 | } 270 | }, 271 | "nbformat": 4, 272 | "nbformat_minor": 4 273 | } 274 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 fMRIAnalysisCource 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # fMRI data analysis course 2 | 3 | Materials for *fMRI data analysis* course taught at [Nicolaus Copernicus University](https://www.umk.pl/en/) in Toruń 4 | for cognitive science students (including first-year students). The course covers fundamental topics in fMRI 5 | data analysis (in Python). 6 | 7 | ### The Hogwarts way 8 | I called the way of teaching the course *the Hogwarts way*. It refers to the fact 9 | that for most participants, the fMRI data analysis, python programming, version control, etc. was like magic at the beginning of the course. 10 | The goal of the course was to introduce topics of fMRI data analysis, reproducibility, 11 | Python, and version control, giving students a lot of **FUN** and freedom. 12 | 13 | 14 | The most important goal of the course was to reduce the initial fear of trying something 15 | entirely new, which is, in fact, quite complex. 16 | 17 | 18 | :warning: **Content warning**: 19 | The course includes a lot of *Harry Potter* content not suitable for muggles 20 | and too serious people. 21 | 22 | ### How to become *neuroinfomagician*? 23 | 24 | ![](images/fMRIDA_study_plan.png) 25 | 26 | 27 | ## General information 28 | 29 | The course was supported by [DataCamp Classroom](datacamp.com/groups/education) and 30 | [GitHub Education](https://education.github.com/). 31 | 32 | 33 | #### Time 34 | - **Lectures and practice**: 30h (split into 3h sessions, every two weeks) 35 | - **Homework** (Jupyter Notebooks share via GitHub Classroom, DataCamp Python courses, poster preparation): ~50h (self work) 36 | - **Total time required to finish the course**: ~80h 37 | 38 | #### Grading 39 | Activity + Homework + Poster + Magic 40 | 41 | 42 | 43 | ## Content 44 | 45 | ### I. [Introduction to neuroimaging](https://github.com/fMRIAnalysisCourse/fmri-analysis-course/blob/master/00-introduction/00-fMRIDA_slides.pdf) & [open science](https://github.com/fMRIAnalysisCourse/fmri-analysis-course/blob/master/01-reproducible_neuroimaging/01-fMRIDA_slides.pdf) (3h) 46 | 47 | ##### Tools: 48 | [Jupyter lab](https://jupyterlab.readthedocs.io/en/stable/), 49 | [Markdown](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet), 50 | [GitHub](https://github.com/) 51 | 52 | ##### Homework: 53 | - GitHub Classroom: [fMRI Q&A in Markdown](https://github.com/fMRIAnalysisCourse/01-homework/blob/master/01-fMRIDA_homework.ipynb) 54 | - DataCamp Classroom: [Introduction to Python](https://learn.datacamp.com/courses/intro-to-python-for-data-science) 55 | - DataCamp Classroom: [Intermediate Python](https://learn.datacamp.com/courses/intermediate-python) 56 | 57 | ### II. [fMRI data manipulation in Python](https://github.com/fMRIAnalysisCourse/fmri-analysis-course/blob/master/02-fmri_data_manipulation_in_python/fMRIDA_27-03-2020.pdf) (3h) 58 | 59 | ##### Tools: 60 | [Matplotlib](https://matplotlib.org/), 61 | [Numpy](https://numpy.org/), 62 | [Nibabel](https://nipy.org/nibabel/), 63 | [Nilearn](https://nilearn.github.io/), 64 | [Pandas](https://pandas.pydata.org/) 65 | 66 | ##### Practice: 67 | - [Structural MRI manipulation](https://github.com/fMRIAnalysisCourse/fmri-analysis-course/blob/master/02-fmri_data_manipulation_in_python/00-structural_mri_manipulation_solution.ipynb) 68 | - [Functional MRI manipulation](https://github.com/fMRIAnalysisCourse/fmri-analysis-course/blob/master/02-fmri_data_manipulation_in_python/01-functional_mri_manipulation_solution.ipynb) 69 | - [Dataframes manipulation in Pandas](https://github.com/fMRIAnalysisCourse/fmri-analysis-course/blob/master/02-fmri_data_manipulation_in_python/02-data_frames_manipulation_solution.ipynb) 70 | 71 | ##### Homework: 72 | - GitHub Classroom: [fMRI data manipulation in Python](https://github.com/fMRIAnalysisCourse/02-homework) 73 | - DataCamp Classroom: [Introduction to Data Visualization with Matplotlib](https://learn.datacamp.com/courses/introduction-to-data-visualization-with-matplotlib) 74 | 75 | ### III. [fMRI data preprocessing](https://github.com/fMRIAnalysisCourse/fmri-analysis-course/blob/master/03-fmri_data_preprocessing/fMRIDA_10-04-2020_%233.pdf) (3h) 76 | ##### Tools: 77 | [Nipype](https://nipype.readthedocs.io/en/latest/), 78 | [Porcupine](https://giraffe.tools/porcupine/TimVanMourik/SomeGiraffeExample), 79 | [fMRIPrep](https://fmriprep.readthedocs.io/en/stable/) 80 | 81 | ##### Practice: 82 | - [Linear transformations in Python](https://github.com/fMRIAnalysisCourse/fmri-analysis-course/blob/master/03-fmri_data_preprocessing/linear_transformations.ipynb) 83 | - [Preprocessing with Nipype](https://github.com/fMRIAnalysisCourse/fmri-analysis-course/blob/master/03-fmri_data_preprocessing/fmri_preprocessing_nipype.ipynb) 84 | 85 | ##### Homework: 86 | - YouTube: [3Blue1Brown: Essence of Linear Algebra](https://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) 87 | - DataCamp Classroom: [Exploratory Data Analysis in Python](https://learn.datacamp.com/courses/exploratory-data-analysis-in-python) 88 | 89 | ### IV. General Linear Model [Part 1](https://github.com/fMRIAnalysisCourse/fmri-analysis-course/blob/master/04-general_linear_model/fMRIDA_24-04-2020_%234.pdf) & [Part 2](https://github.com/fMRIAnalysisCourse/fmri-analysis-course/blob/master/04-general_linear_model/fMRIDA_08-05-2020_%235.pdf) (3h + 3h) 90 | 91 | ##### Tools: 92 | [Nistats](https://nistats.github.io/), 93 | [Nilearn](https://nilearn.github.io/), 94 | [Sklearn](https://scikit-learn.org/stable/) 95 | 96 | ##### Practice: 97 | - [Simple linear regression](https://github.com/fMRIAnalysisCourse/fmri-analysis-course/blob/master/04-general_linear_model/simple_linear_regression.ipynb) 98 | - [Multiple linear regression](https://github.com/fMRIAnalysisCourse/fmri-analysis-course/blob/master/04-general_linear_model/multiple_linear_regression.ipynb) 99 | - [GLM on fMRI data](https://github.com/fMRIAnalysisCourse/fmri-analysis-course/blob/master/04-general_linear_model/glm_on_fMRI_data.ipynb) 100 | 101 | ##### Homework: 102 | - GitHub Classroom: [Linear & multiple linear regression](https://github.com/fMRIAnalysisCourse/03-homework) 103 | - GitHub Classroom: [GLM analysis & fMRI results plotting](https://github.com/fMRIAnalysisCourse/04-homework) 104 | - DataCamp Classroom: [Introduction to Linear Modeling in Python](https://learn.datacamp.com/courses/introduction-to-linear-modeling-in-python) 105 | - DataCamp Classroom: [Statistical Thinking in Python (Part 1)](https://learn.datacamp.com/courses/statistical-thinking-in-python-part-1) 106 | 107 | ### V. [Functional connectivity](https://github.com/fMRIAnalysisCourse/fmri-analysis-course/blob/master/05-functional_connectivity/fMRIDA_05-06-2020_%236.pdf) & [machine learning on fMRI data ](https://github.com/fMRIAnalysisCourse/fmri-analysis-course/blob/master/06-machine_learning/fMRIDA_05-06-2020_%237.pdf) (3h) 108 | ##### Tools: 109 | [Nilearn](https://nilearn.github.io/), 110 | [Scipy](https://www.scipy.org/) 111 | 112 | ##### Practice: 113 | - [Brain parcellations](https://github.com/fMRIAnalysisCourse/fmri-analysis-course/blob/master/05-functional_connectivity/brain_parcellations.ipynb) 114 | - [Seed-seed connectivity](https://github.com/fMRIAnalysisCourse/fmri-analysis-course/blob/master/05-functional_connectivity/seed-seed_connectivity.ipynb) 115 | - [Seed-voxel connectivity](https://github.com/fMRIAnalysisCourse/fmri-analysis-course/blob/master/05-functional_connectivity/seed-voxel_connectivity.ipynb) 116 | - [ML on fMRI data](https://github.com/fMRIAnalysisCourse/fmri-analysis-course/blob/master/06-machine_learning/machine_learning_fMRI.ipynb) 117 | 118 | ##### Homework: 119 | - GitHub Classroom: [Seed-based functional connectivity](https://github.com/fMRIAnalysisCourse/05-homework) 120 | - Poster assignment 121 | -------------------------------------------------------------------------------- /data/harry.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fMRIAnalysisCourse/fmri-analysis-course/5495887620b54bc3f6b5c83ed9fa35df149414c7/data/harry.npy -------------------------------------------------------------------------------- /data/james.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fMRIAnalysisCourse/fmri-analysis-course/5495887620b54bc3f6b5c83ed9fa35df149414c7/data/james.npy -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/fMRIAnalysisCourse/fmri-analysis-course/5495887620b54bc3f6b5c83ed9fa35df149414c7/images/fMRIDA_study_plan.png -------------------------------------------------------------------------------- /solutions/seed-voxel_connectivity_solution.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Seed-voxel functional connectivity" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "## Load libraries" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 1, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "import numpy as np\n", 24 | "from nilearn import datasets\n", 25 | "from nilearn import input_data\n", 26 | "from nilearn.connectome import ConnectivityMeasure\n", 27 | "from nilearn import plotting" 28 | ] 29 | }, 30 | { 31 | "cell_type": "markdown", 32 | "metadata": {}, 33 | "source": [ 34 | "## Load data" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 2, 40 | "metadata": {}, 41 | "outputs": [], 42 | "source": [ 43 | "# Load fMRI data\n", 44 | "dataset = datasets.fetch_development_fmri(n_subjects=1)\n", 45 | "func_filename = dataset.func[0]\n", 46 | "confounds_filename = dataset.confounds[0]" 47 | ] 48 | }, 49 | { 50 | "cell_type": "markdown", 51 | "metadata": {}, 52 | "source": [ 53 | "## Create a seed" 54 | ] 55 | }, 56 | { 57 | "cell_type": "code", 58 | "execution_count": 3, 59 | "metadata": {}, 60 | "outputs": [], 61 | "source": [ 62 | "pcc_coords = [(0, -52, 18)]\n", 63 | "seed_masker = input_data.NiftiSpheresMasker(\n", 64 | " pcc_coords, radius=8,\n", 65 | " detrend=True, standardize=True)" 66 | ] 67 | }, 68 | { 69 | "cell_type": "markdown", 70 | "metadata": {}, 71 | "source": [ 72 | "## Extract timeseries from a seed" 73 | ] 74 | }, 75 | { 76 | "cell_type": "code", 77 | "execution_count": 4, 78 | "metadata": {}, 79 | "outputs": [], 80 | "source": [ 81 | "seed_timeseries = seed_masker.fit_transform(func_filename,\n", 82 | " confounds=confounds_filename)\n", 83 | "seed_timeseries = np.squeeze(seed_timeseries)" 84 | ] 85 | }, 86 | { 87 | "cell_type": "markdown", 88 | "metadata": {}, 89 | "source": [ 90 | "## Estract timeseries from the whole brain" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 5, 96 | "metadata": {}, 97 | "outputs": [], 98 | "source": [ 99 | "brain_masker = input_data.NiftiMasker(\n", 100 | " smoothing_fwhm=8,\n", 101 | " detrend=True, standardize=True,\n", 102 | " memory='nilearn_cache', memory_level=1, verbose=0)\n", 103 | "\n", 104 | "brain_timeseries = brain_masker.fit_transform(func_filename,\n", 105 | " confounds=confounds_filename)" 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": 6, 111 | "metadata": {}, 112 | "outputs": [ 113 | { 114 | "name": "stdout", 115 | "output_type": "stream", 116 | "text": [ 117 | "(168,)\n", 118 | "(168, 32504)\n" 119 | ] 120 | } 121 | ], 122 | "source": [ 123 | "# Print shape of seed time-series\n", 124 | "print(seed_timeseries.shape)\n", 125 | "\n", 126 | "# Print shape of brain time-series\n", 127 | "print(brain_timeseries.shape)" 128 | ] 129 | }, 130 | { 131 | "cell_type": "markdown", 132 | "metadata": {}, 133 | "source": [ 134 | "## Calculate seed to voxel correlations" 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": 7, 140 | "metadata": {}, 141 | "outputs": [], 142 | "source": [ 143 | "# Calculate Pearson product-moment correlation coefficient between seed and all voxel trime-series\n", 144 | "from scipy import stats\n", 145 | "\n", 146 | "corr_map = np.zeros(brain_timeseries.shape[1])\n", 147 | "\n", 148 | "for i in range(brain_timeseries.shape[1]):\n", 149 | " corr_map[i] = stats.pearsonr(seed_timeseries, brain_timeseries[:,i])[0]" 150 | ] 151 | }, 152 | { 153 | "cell_type": "markdown", 154 | "metadata": {}, 155 | "source": [ 156 | "## Plot correlation using statistical map plot" 157 | ] 158 | }, 159 | { 160 | "cell_type": "code", 161 | "execution_count": 12, 162 | "metadata": {}, 163 | "outputs": [ 164 | { 165 | "data": { 166 | "image/png": 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\n", 167 | "text/plain": [ 168 | "
" 169 | ] 170 | }, 171 | "metadata": {}, 172 | "output_type": "display_data" 173 | } 174 | ], 175 | "source": [ 176 | "seed_to_voxel_correlations_img = brain_masker.inverse_transform(corr_map.T)\n", 177 | "\n", 178 | "# Plot correlation map using plot_stat_map\n", 179 | "display = plotting.plot_stat_map(seed_to_voxel_correlations_img, threshold=0.4)\n", 180 | "\n", 181 | "# Add seed ROI marker with display.add_markers()\n", 182 | "display.add_markers(pcc_coords, marker_color='g', marker_size=100)" 183 | ] 184 | } 185 | ], 186 | "metadata": { 187 | "kernelspec": { 188 | "display_name": "Python 3", 189 | "language": "python", 190 | "name": "python3" 191 | }, 192 | "language_info": { 193 | "codemirror_mode": { 194 | "name": "ipython", 195 | "version": 3 196 | }, 197 | "file_extension": ".py", 198 | "mimetype": "text/x-python", 199 | "name": "python", 200 | "nbconvert_exporter": "python", 201 | "pygments_lexer": "ipython3", 202 | "version": "3.8.3" 203 | }, 204 | "widgets": { 205 | "application/vnd.jupyter.widget-state+json": { 206 | "state": {}, 207 | "version_major": 2, 208 | "version_minor": 0 209 | } 210 | } 211 | }, 212 | "nbformat": 4, 213 | "nbformat_minor": 4 214 | } 215 | --------------------------------------------------------------------------------