diff --git a/content/encoding.ipynb b/content/encoding.ipynb deleted file mode 100644 index 1574ae9..0000000 --- a/content/encoding.ipynb +++ /dev/null @@ -1,2524 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "q0TZOcFNqcB1" - }, - "source": [ - "\n", - "# Brain encoding" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "CBK6SsLBrX2S" - }, - "source": [ - "Encoding models try to predict neuronal activity using information from presented stimuli, like an image or sound. Where decoding goes from brain data to real-world stimulus, encoding goes the other direction.\n", - "\n", - "They learn to transform stimuli (typically) into neural response signals. Such models help us understand the relationship between the stimuli and the brain signals (brain's \"code\"). " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "fpkaIo2UMk3K" - }, - "source": [ - "Some questions that encoding models attempt to address :\n", - "\n", - "\n", - "* Does the variation in the response depend on the stimulus?\n", - "\n", - "\n", - "* How well do the responses ‘encode’ the stimuli?\n", - "\n", - "\n", - "* How well are the responses ‘explained’ by the stimuli?\n", - "\n", - "\n", - "* Can some responses be explained by specific stimuli?\n", - "\n", - "\n", - "* How can we quantify the dependence of responses on the stimuli?\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OapDlEXAMyVg" - }, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "CXUTGazQM1UZ" - }, - "source": [ - "In this tutorial, we will consider a study based on Miyawaki et al. 2008. This is based on the nilearn [tutorial on encoding](https://nilearn.github.io/auto_examples/02_decoding/plot_miyawaki_encoding.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tHJkWiUmrYCL" - }, - "source": [ - "## Miyawaki et al. 2008 Study\n", - "\n", - "This example partly reproduces the encoding model presented in Miyawaki, Y., Uchida, H., Yamashita, O., Sato, M.A., Morito, Y., Tanabe, H.C., Sadato, N. and Kamitani, Y., 2008. [`Visual image reconstruction from human brain activity using a combination of multiscale local image decoders`](http://www.cell.com/neuron/abstract/S0896-6273%2808%2900958-6) - *Miyawaki, Y., Uchida, H., Yamashita, O., Sato, M. A., Morito, Y., Tanabe, H. C., ... & Kamitani, Y. Neuron, 60(5), 915-929.*\n", - "\n", - "\n", - "Participants were shown images, which consisted of random 10x10 binary (either black or white) pixels, and the corresponding fMRI activity was recorded. We will try to predict the activity in each voxel from the binary pixel-values of the presented images. \n", - "\n", - "\n", - "We will explore how to build such an **encoding model** in nilearn, predicting **fMRI data** from **visual stimuli**, using the dataset from [`Miyawaki et al., 2008`](http://www.cell.com/neuron/abstract/S0896-6273%2808%2900958-6).\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gLUtb35JQWRv" - }, - "source": [ - "We will first prepare the data for training a model. Then we will understand the regression model and the scoring metric before proceeding to fit the encoding model on the data.\n", - "\n", - "We will evaluate the predictability of the activity at different voxels from the images. We shall explore the nature of prediction and plot these scores as brain maps to draw some inferences. \n", - "\n", - "Then we extract the receptive fields for a set of voxels to see which pixel location a voxel is most sensitive to. We attempt to understand the relationship between the stimuli image pixels and the voxel activities.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oAb-1Q2Ur5lU" - }, - "source": [ - "## Basic Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "VkIBS9qYn59U" - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "import matplotlib.pyplot as plt \n", - "%matplotlib inline\n", - "\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Bz6YLe5KqcB3" - }, - "source": [ - "## Loading the data\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8H4_ajbtuoJy" - }, - "source": [ - "The dataset is readily available in nilearn and can be imported directly. \n", - "\n", - "It can be loaded in the following way." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vhzbqy3en59Z", - "outputId": "270276fd-bd44-4c2d-e264-0d69edb46307" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Dataset created in /root/nilearn_data/miyawaki2008\n", - "\n", - "Downloading data from https://www.nitrc.org/frs/download.php/8486/miyawaki2008.tgz ...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Downloaded 136560640 of 161069109 bytes (84.8%, 0.9s remaining) ...done. (6 seconds, 0 min)\n", - "Extracting data from /root/nilearn_data/miyawaki2008/4356183cd5ae215342603c6edeb89f54/miyawaki2008.tgz..... done.\n" - ] - } - ], - "source": [ - "from nilearn.datasets import fetch_miyawaki2008\n", - "\n", - "dataset = fetch_miyawaki2008()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "foGzhA9UoCFX", - "outputId": "a6ac6924-e819-4319-ef2a-ebabfd45e16a" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'background': '/root/nilearn_data/miyawaki2008/bg.nii.gz',\n", - " 'description': b\"Miyawaki 2008\\n\\n\\nNotes\\n-----\\nCollection of result images from MVPA investigation of the human visual systems.\\n\\nThis :term:`fMRI` study reconstructed visual images by combining local\\nimage bases of multiple scales. Their :term:`contrasts` were independently\\ndecoded from :term:`fMRI` activity by selecting important :term:`voxels` and\\ncapitalizing on their correlation structure.\\n\\nContent\\n-------\\n :'label': Paths to text files containing session and target data\\n :'func': Paths to nifti files with :term:`BOLD` data\\n :'mask': Path to general mask nifti that defines target volume in visual cortex\\n :'mask_roi': List of paths to images with specific data ('RH' for right hemisphere, 'LH' for left hemisphere, 'Vxxx' denote visual areas)\\n\\n\\nReferences\\n----------\\nFor more information on this dataset's structure, see\\nhttp://www.cns.atr.jp/dni/en/downloads/ fmri-data-set-for-visual-image-reconstruction\\n\\nMiyawaki, Y., Uchida, H., Yamashita, O., Sato, M. A.,\\nMorito, Y., Tanabe, H. C., ... & Kamitani, Y. (2008).\\nNeuron, 60(5), 915-929.\\n\\nLicence: unknown.\\n\",\n", - " 'func': ['/root/nilearn_data/miyawaki2008/func/data_figure_run01.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_figure_run02.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_figure_run03.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_figure_run04.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_figure_run05.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_figure_run06.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_figure_run07.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_figure_run08.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_figure_run09.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_figure_run10.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_figure_run11.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_figure_run12.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run01.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run02.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run03.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run04.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run05.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run06.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run07.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run08.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run09.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run10.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run11.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run12.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run13.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run14.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run15.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run16.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run17.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run18.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run19.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run20.nii.gz'],\n", - " 'label': ['/root/nilearn_data/miyawaki2008/label/data_figure_run01_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_figure_run02_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_figure_run03_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_figure_run04_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_figure_run05_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_figure_run06_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_figure_run07_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_figure_run08_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_figure_run09_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_figure_run10_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_figure_run11_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_figure_run12_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run01_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run02_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run03_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run04_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run05_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run06_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run07_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run08_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run09_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run10_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run11_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run12_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run13_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run14_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run15_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run16_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run17_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run18_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run19_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run20_label.csv'],\n", - " 'mask': '/root/nilearn_data/miyawaki2008/mask/mask.nii.gz',\n", - " 'mask_roi': ['/root/nilearn_data/miyawaki2008/mask/LHlag0to1.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/LHlag10to11.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/LHlag1to2.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/LHlag2to3.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/LHlag3to4.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/LHlag4to5.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/LHlag5to6.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/LHlag6to7.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/LHlag7to8.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/LHlag8to9.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/LHlag9to10.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/LHV1d.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/LHV1v.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/LHV2d.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/LHV2v.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/LHV3A.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/LHV3.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/LHV4v.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/LHVP.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/RHlag0to1.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/RHlag10to11.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/RHlag1to2.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/RHlag2to3.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/RHlag3to4.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/RHlag4to5.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/RHlag5to6.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/RHlag6to7.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/RHlag7to8.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/RHlag8to9.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/RHlag9to10.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/RHV1d.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/RHV1v.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/RHV2d.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/RHV2v.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/RHV3A.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/RHV3.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/RHV4v.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/mask/RHVP.nii.gz']}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "d3bdvWDerTUb" - }, - "source": [ - "## Preparing the training data" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "QF_sjaQ2rqIE" - }, - "source": [ - "### Making a list of the necessary files" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NGUO1mBUn59d" - }, - "source": [ - "We only use the training data of this study,\n", - "where random binary images were shown.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "YBI_nRFQn59d" - }, - "outputs": [], - "source": [ - "# Training data starts after the first 12 files\n", - "\n", - "fmri_random_runs_filenames = dataset.func[12:]\n", - "stimuli_random_runs_filenames = dataset.label[12:]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "RynArVyRopdw", - "outputId": "f0ee1c04-0cd0-4916-d91b-243ab9809716" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['/root/nilearn_data/miyawaki2008/func/data_random_run01.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run02.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run03.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run04.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run05.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run06.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run07.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run08.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run09.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run10.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run11.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run12.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run13.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run14.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run15.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run16.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run17.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run18.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run19.nii.gz',\n", - " '/root/nilearn_data/miyawaki2008/func/data_random_run20.nii.gz']" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fmri_random_runs_filenames" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "DUS5lcu4qgXh", - "outputId": "22f8e8f7-f094-4714-8742-ed18f7c38b92" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['/root/nilearn_data/miyawaki2008/label/data_random_run01_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run02_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run03_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run04_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run05_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run06_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run07_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run08_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run09_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run10_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run11_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run12_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run13_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run14_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run15_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run16_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run17_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run18_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run19_label.csv',\n", - " '/root/nilearn_data/miyawaki2008/label/data_random_run20_label.csv']" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "stimuli_random_runs_filenames" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "mscPHDS8ry-z" - }, - "source": [ - "### fMRI data : Preprocessing, Masking and Data extraction" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GKQMajJwqcB4" - }, - "source": [ - "We can use `nilearn.input_data.MultiNiftiMasker` to load the fMRI\n", - "data, clean and mask it.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "eMBhxs8iqcB5" - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "from nilearn.input_data import MultiNiftiMasker\n", - "\n", - "masker = MultiNiftiMasker(mask_img=dataset.mask, detrend=True,\n", - " standardize=True)\n", - "masker.fit()\n", - "fmri_data = masker.transform(fmri_random_runs_filenames)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UzGHXkOUsZ24" - }, - "source": [ - "For each run, the dimensions of the masked data matrix is : \n", - "\n", - "`#samples` x `#voxels`" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4AHuQBFAsbMt", - "outputId": "5248ec98-1b7c-4873-c7a5-fddbedf970ad" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# runs in fmri data = 20\n" - ] - } - ], - "source": [ - "print('# runs in fmri data = ',len(fmri_data))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "OWrtlHMuqcB5", - "outputId": "e94cd205-af22-4e7c-ee19-30a6213d1171" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dimensions of fmri data (#samples, #voxels) = (145, 5438)\n" - ] - } - ], - "source": [ - "print('Dimensions of fmri data (#samples, #voxels) = ', fmri_data[0].shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pW5dV5XlwEjY" - }, - "source": [ - "Let's take a look at some of these BOLD response timecourses:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 730 - }, - "id": "uRoT_wV59E5m", - "outputId": "1f64151e-d606-45ec-8811-4054b165b3bb" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "runs = [5,10]\n", - "voxels = [100,200]\n", - "\n", - "num_voxels = len(voxels)\n", - "\n", - "plt.figure(figsize=(16,6*num_voxels))\n", - "\n", - "for i in range(num_voxels):\n", - " plt.subplot(num_voxels, 1, i+1)\n", - " plt.xlabel('TR')\n", - " plt.ylabel('Normalized BOLD')\n", - " plt.title('Run {:d}, Voxel {:d}'.format(runs[i],voxels[i]))\n", - " plt.plot(fmri_data[runs[i]][:,voxels[i]]);" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "mrOKceAvsDyj" - }, - "source": [ - "### Stimuli data: Data Extraction\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "id": "7YEYusMRq0DY" - }, - "outputs": [], - "source": [ - "stimulus_shape = (10, 10)\n", - "\n", - "# We load the visual stimuli from csv files\n", - "stimuli_data = []\n", - "for stimulus_run in stimuli_random_runs_filenames:\n", - " stimuli_data.append(np.reshape(np.loadtxt(stimulus_run,\n", - " dtype=np.int, delimiter=','),\n", - " (-1,) + stimulus_shape, order='F'))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kdY6kNNstwbR" - }, - "source": [ - "For each run, the dimensions of the masked data matrix is : \n", - "\n", - "`#samples` x `#voxels`" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "V1RrgEK-twbR", - "outputId": "58e59598-5d6f-4771-aedb-81b5a6444eac" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# runs in stimuli data = 20\n" - ] - } - ], - "source": [ - "print('# runs in stimuli data = ',len(stimuli_data))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "6gN5riiYtwbR", - "outputId": "499a7a7f-820c-4206-97fd-693a8713aa50" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dimensions of stimuli data (#samples, image_height, image_width) = (145, 10, 10)\n" - ] - } - ], - "source": [ - "print('Dimensions of stimuli data (#samples, image_height, image_width) = ', stimuli_data[0].shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "G1M9nR1cn59l" - }, - "source": [ - "Let's take a look at some of these binary images:\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 675 - }, - "id": "ecpBM2pMn59m", - "outputId": "1611271e-20f8-434a-d601-666b5c4881aa" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "runs = [1,3,8]\n", - "trials = [27,125,102]\n", - "\n", - "num_runs = len(runs)\n", - "num_images = len(trials)\n", - "\n", - "subplot_index = 1\n", - "\n", - "plt.figure(figsize=(4*num_images, 4*num_runs))\n", - "\n", - "for i in range(num_runs):\n", - " for j in range(num_images):\n", - " plt.subplot(num_runs, num_images, subplot_index)\n", - " plt.imshow(stimuli_data[runs[i]][trials[j]], interpolation='nearest', cmap='gray')\n", - " plt.axis('off')\n", - " plt.title('Run {}, Stimulus Trial {}'.format(runs[i],trials[j]))\n", - " subplot_index += 1\n", - "\n", - "plt.subplots_adjust(wspace=0.5)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MN5zi4IBwsQ9" - }, - "source": [ - "### Creating the input and target matrices for training" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9GlvuvulxTRu" - }, - "source": [ - "The input matrix and target matrix required for fitting the encoding model are prepared as follows:\n", - "\n", - "As fMRI BOLD is a very slow signal, we ensure that the fMRI response data corresponds to the stimulus image shown at a given time by shifting the output sequence by a fixed number of steps (=2)\n", - "\n", - "* We get rid of the fMRI data of the first 2 trials (TRs) is each run\n", - "* We remove the stimuli data of the last 2 trials (TRs) for to offset this delay.\n", - "\n", - "* Both `fmri_data` and `stimuli_data` that are lists of numpy arrays are packaged into a numpy array." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "id": "pYCmNlOun59p" - }, - "outputs": [], - "source": [ - "# Delay chosen based on the TR duration to expect a hemodynamic response\n", - "\n", - "delay_trials = 2\n", - "\n", - "fmri_data = np.vstack([fmri_run[delay_trials:] for fmri_run in fmri_data])\n", - "stimuli_data = np.vstack([stimuli_run[:-delay_trials] for stimuli_run in stimuli_data]).astype(float)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jH17dCZXyvI6" - }, - "source": [ - "Shape of the feature (data) and target matrices" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "z0n2f6w0qgJ1", - "outputId": "2263ee6b-8541-4359-c84f-b51b90cb48b1" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "stimuli_data.shape = (2860, 10, 10)\n", - "fmri_data.shape = (2860, 5438)\n" - ] - } - ], - "source": [ - "print('stimuli_data.shape =',stimuli_data.shape)\n", - "print('fmri_data.shape =',fmri_data.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3QRC9Dnyy0_u" - }, - "source": [ - "Each sample in `stimuli_data` has 2 components and this needs to converted to one before fitting the model.\n", - "\n", - "`(10,10)` dimensions need to be flattened into 100-dimensional vectors." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "-gSnLPg6n59x", - "outputId": "ad9c5579-7b3e-454b-f222-e2346bc2a544" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "stimuli_data.shape = (2860, 100)\n" - ] - } - ], - "source": [ - "# Flatten the stimuli\n", - "\n", - "stimuli_data = np.reshape(stimuli_data, (-1, stimulus_shape[0] * stimulus_shape[1]))\n", - "\n", - "print('stimuli_data.shape =',stimuli_data.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qOReYhcFzgDx" - }, - "source": [ - "## Building the encoding models\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oLDHWI0FweUP" - }, - "source": [ - "We can now proceed to build a simple **voxel-wise encoding model** using [Ridge regression](http://en.wikipedia.org/wiki/Tikhonov_regularization).\n", - "\n", - "For each voxel we fit an independent regression model, using the pixel-values of the visual stimuli to predict the BOLD activity in this voxel.\n", - "\n", - "We need to understand the model and a way to score our predictions to quantify the estimation." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "km2Vhfebjzm3" - }, - "source": [ - "### Ridge Regression Model" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "r9fMvqf-j0LV" - }, - "source": [ - "For the learning task in encoding, the data matrix for training is not always ideal.\n", - "\n", - "Especially in most neroimaging studies, it is very common the have the number of samples of the stimuli lesser the dimensionality of the data.\n", - "\n", - "This causes problems in fitting the model and the computations involved. The training might not be able to yield a good model.\n", - "\n", - "A better version of the linear regression that handles such issues better is Ridge. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6vqbUY7Vs8N4" - }, - "source": [ - "\n", - "The analytical solution to compute the is given by :\n", - "$$ \\mathbf{\\beta} = (\\mathbf{X}^{T}\\mathbf{X} + \\alpha \\mathbf{I})^{-1} \\mathbf{X}^{T} \\mathbf{y}$$\n", - "\n", - "The $\\alpha$ value controls the stability of this estimation and needs to be carefully chosen." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "U3vXb2CRj1wW" - }, - "source": [ - "The parameter matrix $\\mathbf{\\beta}$ can also be calculated using optimization.\n", - "\n", - "The mean squared error ($MSE$) objective function of ordinary linear regression is augmented with an extra term to reduce the magnitude of the coefficients ${\\beta_i}^{(k)}$ in the matrix $\\mathbf{\\beta}$. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tT9vEeviXgQx" - }, - "source": [ - "We will use the [`Ridge`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html) model from scikit-learn." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Np1H52WJkGK-" - }, - "source": [ - "### $R^2$ score for evaluation of regression" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_s2XZlgwkGeF" - }, - "source": [ - "For the actual target values ${y}_i$ the regression model attempts to predict a good estimates $\\hat{y}_i$ and the error of such a model prediction can be expressed by the mean squared error ($MSE$) which is the mean of all the squared error terms.\n", - "\n", - "\n", - "$$MSE(y,\\hat{y})~=~ \\frac{1}{n} \\sum_{i=1}^{n}~(y_i - \\hat{y}_i)^2 ~=~ \\frac{1}{n} ~SSE_{regression}$$\n", - "\n", - "\n", - "It is an absolute value and thus difficult to infer the quality of the regression fit. The $MSE$ of different model fits can practically be any real number.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MFIY_q3nqEhJ" - }, - "source": [ - "The mean response of the target values can be considered as a good baseline for the regression model to estimate. So, such a baseline model always predicts he value $\\bar{y}$ for all inputs where $\\bar{y} ~=~ \\frac{1}{n} \\sum_{i=1}^{n}~y_i$.\n", - "\n", - "We expect that any model that we train to perform atleast as good as thise mean baseline model. That is to say that if a model always outputs the mean of the responses in the training set as its prediction, the sum of squared errors of such a model is given by $SSE_{total} ~=~\\sum_{i=1}^{n}~(y_i - \\bar{y})^2$ which is precisely the variance of the values $y_i$\n", - "\n", - "This value is the upper limit for the error that a model can incur in its predictions. We can expres the error of any model as a quantity relative to this.\n", - "\n", - "
\n", - "\n", - "$$R^2(y,\\hat{y})~=~ 1 - \\frac{\\sum_{i=1}^{n}~(y_i - \\hat{y}_i)^2}{\\sum_{i=1}^{n}~(y_i - \\bar{y})^2}$$\n", - "\n", - "
\n", - "\n", - "$$R^2(y,\\hat{y})~=~ 1 - \\frac{SSE_{regression}}{SSE_{total}} ~=~ 1 - \\frac{Unexplained~Variance}{Total~Variance} ~=~ \\frac{Explained~Variance}{Total~Variance}$$\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "T2a6gVMImx9u" - }, - "source": [ - "This quantity $R^2$ (that is equivalently referred to as R2) is a relative measure that is usually between $0$ and $1$ and can be interpreted as aprportion of explained variance (can also be expressed as a percentage).\n", - "\n", - "This score is very useful in determining if " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OeqgzNh-XtrQ" - }, - "source": [ - "We make use of the [`r2_score`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html) function in scikit-lean to compute this for each voxel during the model evaluation." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ZPJvExQyVmLL" - }, - "source": [ - "### Cross-Validation" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YJlO-bc1VmZE" - }, - "source": [ - "The evaluation of regression model used in encoding needs to be done for truly “unseen” inputs. Otherwise, the model could easily overfit to the stimuli/features seen during training and could perform poorly in general and can signal an association that is not true.\n", - "\n", - "\n", - "Cross-validation methods (k-Fold, LOOCV, etc) can be used to get a good estimate of the generalization of prediction of the regression model. As these methods fit models several times and average the results, these are more reliable.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8iAMIgVyXumx" - }, - "source": [ - "The [`KFold`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html) class in scikit-learn helps us create the splits for thse different folds." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "CB48zNqptv4n" - }, - "source": [ - "### Training the model" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "id": "EK2TyoBuzgDx" - }, - "outputs": [], - "source": [ - "from sklearn.linear_model import Ridge\n", - "from sklearn.model_selection import KFold" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "t8uCRQfkzgDx" - }, - "source": [ - "Using 10-fold cross-validation, we partition the data into 10 'folds'. \n", - "\n", - "We hold out each fold of the data for testing, then fit a ridge regression to the remaining 9/10 of the data, using `stimuli_data` as predictors and `fmri_data` as targets, and create predictions for the held-out 10th fold.\n", - "\n", - "In each fold, we compute the $R^2$ scores of the predictions at each voxel and store the values for further analysis.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "McygkvDPzgDx", - "outputId": "75479119-3ecc-40f0-b531-9e07046bb2dc" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fold = [0] :: #train samples = 2574, #test samples = 286\n", - "fold = [1] :: #train samples = 2574, #test samples = 286\n", - "fold = [2] :: #train samples = 2574, #test samples = 286\n", - "fold = [3] :: #train samples = 2574, #test samples = 286\n", - "fold = [4] :: #train samples = 2574, #test samples = 286\n", - "fold = [5] :: #train samples = 2574, #test samples = 286\n", - "fold = [6] :: #train samples = 2574, #test samples = 286\n", - "fold = [7] :: #train samples = 2574, #test samples = 286\n", - "fold = [8] :: #train samples = 2574, #test samples = 286\n", - "fold = [9] :: #train samples = 2574, #test samples = 286\n" - ] - } - ], - "source": [ - "from sklearn.metrics import r2_score\n", - "\n", - "cv = KFold(n_splits=10)\n", - "\n", - "scores = []\n", - "for fold, (train, test) in enumerate(cv.split(X=stimuli_data)):\n", - " print('fold = [{:d}] :: #train samples = {:d}, #test samples = {:d}'.format(fold,len(train),len(test)))\n", - "\n", - " # create a ridge model\n", - " model = Ridge(alpha=100.)\n", - "\n", - " # train the Ridge model on the training set\n", - " model.fit(stimuli_data[train], fmri_data[train])\n", - "\n", - " # and predict the fMRI activity for the test set\n", - " predictions = model.predict(stimuli_data[test])\n", - "\n", - " # we compute how much variance our encoding model explains in each voxel\n", - " scores.append(r2_score(fmri_data[test], predictions, multioutput='raw_values'))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "r6_BTmAItqds" - }, - "source": [ - "### Analyzing the predictions " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OHhH5hqOuBwl" - }, - "source": [ - "We will consider the predicted signals from the last fold of the k-fold that is found in the `predictions` variable and take the index values from the actual `fmri_data` that correspond to the original data.\n", - "\n", - "From this we will consider 1 run from the test set of the final fold (which contains 2 runs).\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "LIxLCGm0qUys", - "outputId": "f9378e44-0828-401b-9049-a143dc6a3c83" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " actual.shape (#TRs,#voxels) = (143, 5438)\n", - "predicted.shape (#TRs,#voxels) = (143, 5438)\n" - ] - } - ], - "source": [ - "#Extract only the first run from the test set and predictions\n", - "\n", - "actual = fmri_data[test[:len(test)//2]]\n", - "\n", - "predicted = predictions[:len(predictions)//2,:]\n", - "\n", - "print(' actual.shape (#TRs,#voxels) = ',actual.shape)\n", - "print('predicted.shape (#TRs,#voxels) = ',predicted.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "HuXXIdiZzg0v" - }, - "source": [ - "We will plot the predicted BOLD signals along with the actual BOLD signals for different voxels as a timecourse visualization.\n", - "\n", - "Note that some voxels have a relatively better prediction (voxels 1950, 1951, 1952,etc.), while few others have very bad predictions (voxels 0, 1, 2, 840, 841, etc)." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "RERjmcPDsOvx", - "outputId": "daa0b255-f9d8-49df-8175-8e5b9b3c42f4" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "voxels = [1951,2272,841]\n", - "\n", - "num_voxels = len(voxels)\n", - "\n", - "plt.figure(figsize=(16,6*num_voxels))\n", - "\n", - "for i in range(num_voxels):\n", - " plt.subplot(num_voxels, 1, i+1)\n", - " plt.xlabel('TR')\n", - " plt.ylabel('Normalized BOLD')\n", - " plt.title('Voxel {:d}'.format(voxels[i]))\n", - " plt.plot(actual[:,voxels[i]], label='actual')\n", - " plt.plot(predicted[:,voxels[i]], label='predicted')\n", - " plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OLUY_Q0M0DmA" - }, - "source": [ - "#### Scatter Plot of actual vs predicted values" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 391 - }, - "id": "oXZb2FwXukNm", - "outputId": "5cadfc56-2c26-46b9-fef9-a7d6ebd7395c" - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "voxel = 1956\n", - "\n", - "plt.figure(figsize=(6,6))\n", - "plt.ylabel('predicted BOLD')\n", - "plt.xlabel('actual BOLD')\n", - "\n", - "lims = [\n", - " np.min([plt.xlim(), plt.ylim()]), # min of both axes\n", - " np.max([plt.xlim(), plt.ylim()]), # max of both axes\n", - "]\n", - "\n", - "# x=y line for reference\n", - "plt.plot(lims, lims, '--', alpha=0.75, zorder=0)\n", - "\n", - "plt.scatter(actual[:,voxel],predicted[:,voxel]);" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3_Qm6w4q5hfq" - }, - "source": [ - "Computing the $R^2$ score for a specific voxel.\n", - "\n", - "The Pearson r is also computed for reference." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "C5nYkxZX5Xe8", - "outputId": "71430a9f-62cb-4e85-deda-52340a46b599" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pearson r at voxel 1951 = 0.811\n", - "R2 score at voxel 1951 = 0.657\n" - ] - } - ], - "source": [ - "from scipy.stats import pearsonr\n", - "\n", - "voxel = 1951\n", - "\n", - "pearson_r, _ = pearsonr(actual[:,voxel],predicted[:,voxel])\n", - "r2 = r2_score(actual[:,voxel],predicted[:,voxel])\n", - "\n", - "print('Pearson r at voxel {} = {:.3f}'.format(voxel,pearson_r))\n", - "print('R2 score at voxel {} = {:.3f}'.format(voxel,r2))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "fRuF4mf8zgDx" - }, - "source": [ - "## Mapping the encoding scores on the brain\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "1llcZk28OMtj" - }, - "source": [ - "To plot the scores onto the brain, we create a Nifti1Image containing the scores and then threshold it:\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5PEp41ipJ22G" - }, - "source": [ - "### Computation of the score map and generating the brain image" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TLgfWLE3KuJm" - }, - "source": [ - "The `masker` object used to map the voxels to the data matrix is reused to inverse transform the score matrix to the brain volumne. " - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "id": "399ex2FLzgDx" - }, - "outputs": [], - "source": [ - "from nilearn.image import threshold_img\n", - "\n", - "# Average the score maps accross folds\n", - "score_map = np.mean(scores, axis=0)\n", - "\n", - "# Ignore the negative score values \n", - "score_map[score_map < 0] = 0\n", - "\n", - "# Bring the scores into the shape of the background brain\n", - "score_map_img = masker.inverse_transform(score_map)\n", - "\n", - "thresholded_score_map_img = threshold_img(score_map_img, threshold=1e-6, copy=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jcG6jrTqIQ_k" - }, - "source": [ - "The size of the `score_map` represents the number of voxels for which the scores have been computed." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "RYMLFdeZzgDy", - "outputId": "fdfefa32-d3a3-454c-8f65-801138cf5ca8" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The score_map is of size = (5438,)\n" - ] - } - ], - "source": [ - "print('The score_map is of size = ',score_map.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tUkRczWr8XiY" - }, - "source": [ - "Finding the voxel with the best prediction" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Rks3mL6ord5W", - "outputId": "90e36a9c-8a2d-4055-96b9-299f0500ee04" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best voxel 1951 with score 0.6006\n" - ] - } - ], - "source": [ - "best_score = np.max(score_map)\n", - "best_voxel_ix = np.argmax(score_map)\n", - "\n", - "print('Best voxel {} with score {:.4f}'.format(best_voxel_ix,best_score))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4fWfTqk48bdH" - }, - "source": [ - "Finding top k voxels with the best predictive scores." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - }, - "id": "d4xwvHF91NLO", - "outputId": "747b953a-ebb9-44b3-efc8-d512a1feecf8" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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voxelscore
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" - ], - "text/plain": [ - " voxel score\n", - "1 1951 0.60\n", - "2 2272 0.60\n", - "3 1780 0.60\n", - "4 2284 0.58\n", - "5 2282 0.56\n", - "6 1411 0.56\n", - "7 1424 0.56\n", - "8 2120 0.55\n", - "9 1573 0.55\n", - "10 1588 0.55" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "# Number of top/bottom voxels to select\n", - "k = 10\n", - "\n", - "# Pick the top voxel indexes and their scores\n", - "top_voxels = np.argsort(score_map)[:-(k+1):-1]\n", - "top_scores = score_map[top_voxels]\n", - "\n", - "# Organize the data into a dataframe for better visualization\n", - "df = pd.DataFrame()\n", - "df['voxel'] = top_voxels.astype(int)\n", - "df['score'] = top_scores.round(2)\n", - "df.index += 1\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ul8dbBXOSuMq" - }, - "source": [ - "#### Histogram of the $R^2$ scores" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "M2-aWq9PSy8a" - }, - "source": [ - "We notice that only a small minority of voxels have higher $R^2$ score and these are the voxels that are more predictive for the input features." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 412 - }, - "id": "UdOnNHBwLorT", - "outputId": "c84fe2db-dda0-4bcf-cb6a-e8456e2e14a9" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(6,6))\n", - "plt.xlabel(r'$R^2$ value')\n", - "plt.ylabel('#voxels')\n", - "plt.title(r'Histogram of $R^2$ values across voxels')\n", - "plt.hist(score_map);" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "0yiJJSj1LDmw" - }, - "source": [ - "### Plotting the score map brain image" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TuQ_2cKxZXnC" - }, - "source": [ - "We can plot first plot the statistical map of the $R^2$ scores in an interactive plot." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 289 - }, - "id": "jdu036i1GvBB", - "outputId": "f38483f4-6de3-4f96-d58f-63a5e7e91b4a" - }, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from nilearn import image\n", - "from nilearn import plotting\n", - "\n", - "plot = plotting.view_img(thresholded_score_map_img, draw_cross=False, symmetric_cmap=False, cmap=plotting.cm.black_red)\n", - "plot" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "b8kUi1PrzgDy" - }, - "source": [ - "We them plotting the statistical map on a background brain along only z for closer analysis.\n", - "We mark four voxels (with outlines blue, green, red, pink) which we will inspect more closely in the next section.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 559 - }, - "id": "xiqnvwGbzgDy", - "outputId": "45c2eed7-3554-470f-fd8c-66848586c263" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from nilearn.plotting import plot_stat_map\n", - "from nilearn.image import coord_transform\n", - "\n", - "def index_to_xy_coord(x, y, z=10):\n", - " '''Transforms data index to coordinates of the background + offset'''\n", - " coords = coord_transform(x, y, z,\n", - " affine=thresholded_score_map_img.affine)\n", - " return np.array(coords)[np.newaxis, :] + np.array([0, 1, 0])\n", - "\n", - "\n", - "xy_indices_of_special_voxels = [(30, 10), (32, 10), (31, 9), (31, 10)]\n", - "\n", - "display = plot_stat_map(thresholded_score_map_img, bg_img=dataset.background,\n", - " cut_coords=[-8], display_mode='z')\n", - "\n", - "# creating a marker for each voxel and adding it to the statistical map\n", - "\n", - "for i, (x, y) in enumerate(xy_indices_of_special_voxels):\n", - " display.add_markers(index_to_xy_coord(x, y), marker_color='none',\n", - " edgecolor=['b', 'r', 'magenta', 'g'][i],\n", - " marker_size=140, marker='s',\n", - " facecolor='none', lw=4.5)\n", - "\n", - "\n", - "# Re-set figure size after construction so colorbar gets rescaled too\n", - "fig = plt.gcf()\n", - "fig.set_size_inches(8, 8)\n", - "#fig.suptitle('Receptive fields of the marked voxels', fontsize=25)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JmNQ8Y1hZzHP" - }, - "source": [ - "* We see that all voxels do not respond in the same way to the inputs. The relative prediction scores in different locations for this input can be used to draw some inferences.\n", - "\n", - "* We can observe that among the visual areas defined by the mask, only few locations deem. These seem to be concentrated around the visual cortices. This means that the voxels in these regions could potentially encode these fine-grained features such as pixels and/or their positions. It is known from years of study that the early visual areas such as V1, V2, etc. encode such features.\n", - "\n", - "* There are other areas in VT that are not very predictable from the stimulus pixels. This could either mean that the voxels at these locations do not respond depending on these type of stimuli (or) that they do not encode these pixel-level features of the stimuli. For example, they could be more sensitive to images of faces, places, etc. or higher-level concepts/patterns unlike the scrambled image patterns in the stimuli.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "heGLV5xjn5-A" - }, - "source": [ - "## Estimating receptive fields\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WN2lDuGIiE-t" - }, - "source": [ - "\n", - "\n", - "A voxel's [`receptive field`](http://en.wikipedia.org/wiki/Receptive_field) is the region of a stimulus (like an image) where the presence of an object,\n", - "like a white instead of a black pixel, results in a change in activity in the voxel. \n", - "\n", - "In our case the receptive field is just the vector of 100 regression coefficients (one for each pixel) reshaped into the 10x10 form of the original images. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LU5PmdBlgaIm" - }, - "source": [ - "### Receptive fields from the Ridge model" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MDttka7zcsMy" - }, - "source": [ - "The `model` variable consists of the model fit from the last fold (10th) rom the previous section. Ideally, we average or pick the best fold for a model. But this is good enough for analyzing the coefficients. \n", - "\n", - "For a given voxel, the coefficients of the linear model can be accessed to obtain 100 dimensional weights that can be visulaized on 10x10 grid corresponding to the stimulus image." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 403 - }, - "id": "6Ybc5MHrgHwD", - "outputId": "d3be1802-7947-459c-9b09-0a9773f68e45" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "\n", - "voxels = [1952,1424,1780,0]\n", - "\n", - "num_images = len(voxels)\n", - "\n", - "plt.figure(figsize=(6*num_images, 6))\n", - "\n", - "for j in range(num_images):\n", - " plt.subplot(1, num_images, j+1)\n", - " plt.imshow(model.coef_[voxels[j]].reshape(10,10))\n", - " plt.axis('off')\n", - " plt.title('Voxel {}'.format(voxels[j]))\n", - " plt.colorbar()\n", - "\n", - "plt.subplots_adjust(wspace=0.25)\n", - "plt.suptitle('Receptive fields of some random voxels predicted by the Ridge model');" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vDnL4U5eio9x" - }, - "source": [ - "We see that some voxels such as (1952,1424,etc.) have a peak value at one or two pixels only and most other pixel weights are closer to zero.\n", - "\n", - "Whereas for other voxels such as (0, 60, etc.) there is no clear pattern or sellectivity and also the maximum value is very low." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BgZ9C8moipe9" - }, - "source": [ - "Let us now take a look at the receptive fields of the four marked voxels:" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 400 - }, - "id": "gTRzdzeKibDl", - "outputId": "58790ab6-64ed-4192-b6a8-3cbce6125ed1" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "\n", - "voxels = [1780, 1951, 2131, 1935] \n", - "\n", - "num_images = len(voxels)\n", - "\n", - "plt.figure(figsize=(6*num_images, 6))\n", - "\n", - "for j in range(num_images):\n", - " plt.subplot(1, num_images, j+1)\n", - " plt.imshow(model.coef_[voxels[j]].reshape(10,10))\n", - " plt.axis('off')\n", - " plt.title('Voxel {}'.format(voxels[j]))\n", - " plt.colorbar()\n", - "\n", - "plt.subplots_adjust(wspace=0.25)\n", - "plt.suptitle('Receptive fields of the 4 marked voxels predicted by the Ridge model');" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3wGHuCgRcsX1" - }, - "source": [ - "We see that rach of them have a strong selectivity to specific pixel locations, while they do not seem to attend to th other pixels." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oT_XdxrQcxcY" - }, - "source": [ - "### Receptive fields from Lasso model" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "USzzoVfwcsg9" - }, - "source": [ - "Now we take a closer look at the receptive fields of the four marked voxels.\n", - "\n", - "Some voxels are receptive to only very few pixels, so we use [`Lasso regression`](http://en.wikipedia.org/wiki/Lasso_(statistic)) to estimate a sparse\n", - "set of regression coefficients. We do so to eliminate any insignficant weights to other pixels and see if we recover similar selectivities.\n", - "\n", - "Lasso not only minimizes the values of coefficients, it also brings some of them to zero. This helps us get a clear and distinct picture of the pixel selectivities. \n", - "\n", - "We will use the [`LassoLarsCV`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoLarsCV.html) model from scikit-learn for this and plot the receptive fields of the 4 selected voxels. This procedure is very similar to the Ridge fit, but it is done automatically.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 529 - }, - "id": "MNQJokfPn5-A", - "outputId": "239561e2-db97-475f-aed9-a3455419feac" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from sklearn.linear_model import LassoLarsCV\n", - "\n", - "# Automatically estimate the sparsity by cross-validation\n", - "lasso = LassoLarsCV(max_iter=10)\n", - "\n", - "# Index values of the voxels marked above\n", - "voxels = [1780, 1951, 2131, 1935]\n", - "\n", - "# Mark the same pixel in each receptive field\n", - "marked_pixel = (4, 2)\n", - "\n", - "from matplotlib import gridspec\n", - "from matplotlib.patches import Rectangle\n", - "\n", - "fig = plt.figure(figsize=(12, 8))\n", - "fig.suptitle('Receptive fields of the marked voxels', fontsize=25)\n", - "\n", - "# GridSpec allows us to do subplots with more control of the spacing\n", - "gs = gridspec.GridSpec(2, 3)\n", - "\n", - "# Fit the Lasso for each of the three voxels of the upper row\n", - "for i, index in enumerate(voxels[:3]):\n", - "\n", - " ax = plt.subplot(gs[0, i])\n", - "\n", - " # Compute receptive field by fitting a lasso model for the encoding \n", - " # Reshape the coefficients into the form of the original images\n", - "\n", - " lasso_fit = lasso.fit(stimuli_data, fmri_data[:, index])\n", - " rf = lasso_fit.coef_.reshape((10, 10))\n", - "\n", - " # add a black background\n", - " ax.imshow(np.zeros_like(rf), vmin=0., vmax=1., cmap='gray')\n", - " ax_im = ax.imshow(np.ma.masked_less(rf, 0.1), interpolation=\"nearest\",\n", - " cmap=['Blues', 'Greens', 'Reds'][i], vmin=0., vmax=0.75)\n", - " # add the marked pixel\n", - " ax.add_patch(Rectangle(\n", - " (marked_pixel[1] - .5, marked_pixel[0] - .5), 1, 1,\n", - " facecolor='none', edgecolor='r', lw=4))\n", - " plt.axis('off')\n", - " plt.colorbar(ax_im, ax=ax)\n", - "\n", - "# and then for the voxel at the bottom\n", - "\n", - "gs.update(left=0., right=1., wspace=0.1)\n", - "ax = plt.subplot(gs[1, 1])\n", - "# Reshape the coefficients into the form of the original images\n", - "lasso_fit = lasso.fit(stimuli_data, fmri_data[:, voxels[3]])\n", - "rf = lasso_fit.coef_.reshape((10, 10))\n", - "\n", - "ax.imshow(np.zeros_like(rf), vmin=0., vmax=1., cmap='gray')\n", - "ax_im = ax.imshow(np.ma.masked_less(rf, 0.1), interpolation=\"nearest\",\n", - " cmap='RdPu', vmin=0., vmax=0.75)\n", - "\n", - "# add the marked pixel\n", - "ax.add_patch(Rectangle(\n", - " (marked_pixel[1] - .5, marked_pixel[0] - .5), 1, 1,\n", - " facecolor='none', edgecolor='r', lw=4))\n", - "plt.axis('off')\n", - "plt.colorbar(ax_im, ax=ax);" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "T7mxU871n5-D" - }, - "source": [ - "The receptive fields of the four voxels are not only close to each other,\n", - "the relative location of the pixel each voxel is most sensitive to\n", - "roughly maps to the relative location of the voxels to each other.\n", - "We can see a relationship between some voxel's receptive field and\n", - "its location in the brain.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ghgvO95UXyp7" - }, - "source": [ - "## Evaluation of the encoding model on a test dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FfUSd5syX8o5" - }, - "source": [ - "We will now use another set of runs which used slightly different stimuli distribution of \"figures\" (non-random images) as opposed to the random pattern images that it was trained on.\n", - "\n", - "There are the first 12 runs in both the fmri and stimulus data." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "W7Rw7_n8eMKz" - }, - "source": [ - "### Data Preparation " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ZFx3unSseaXc" - }, - "source": [ - "We will do the same procedure for extracting the data and preparing the data matrices for the evaluation." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SBmYwty1Y_65" - }, - "source": [ - "Getting the file paths of these data." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "id": "O941NppvUn6_" - }, - "outputs": [], - "source": [ - "# Training data is the first 12 files\n", - "\n", - "fmri_figure_runs_filenames, stimuli_figure_runs_filenames = dataset.func[:12], dataset.label[:12]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "q_NSvAqMZIrF" - }, - "source": [ - "Using the masker to extract the fmri time series data of the \"figure\" runs." - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": { - "id": "3zBargYCUn6_" - }, - "outputs": [], - "source": [ - "fmri_figure_data = masker.transform(fmri_figure_runs_filenames)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "f3UZv4yQb42s" - }, - "source": [ - "Extract the stimulus images of the \"figure\" runs." - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "id": "FflQst7yUn6_" - }, - "outputs": [], - "source": [ - "stimulus_shape = (10, 10)\n", - "\n", - "# We load the visual stimuli from csv files\n", - "stimuli_figure_data = []\n", - "for stimulus_run in stimuli_figure_runs_filenames:\n", - " stimuli_figure_data.append(np.reshape(np.loadtxt(stimulus_run,\n", - " dtype=np.int, delimiter=','),\n", - " (-1,) + stimulus_shape, order='F'))" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 675 - }, - "id": "aysFFW67hr70", - "outputId": "6be9dbe0-1623-4724-a988-98f1913ee43a" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import pylab as plt\n", - "\n", - "runs = [1,3,8]\n", - "trials = [27,82,118]\n", - "\n", - "num_runs = len(runs)\n", - "num_images = len(trials)\n", - "\n", - "subplot_index = 1\n", - "\n", - "plt.figure(figsize=(4*num_images, 4*num_runs))\n", - "\n", - "for i in range(num_runs):\n", - " for j in range(num_images):\n", - " plt.subplot(num_runs, num_images, subplot_index)\n", - " plt.imshow(stimuli_figure_data[runs[i]][trials[j]], interpolation='nearest', cmap='gray')\n", - " plt.axis('off')\n", - " plt.title('Run {}, Stimulus Trial {}'.format(runs[i],trials[j]))\n", - " subplot_index += 1\n", - "\n", - "plt.subplots_adjust(wspace=0.5)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "S_IVxEZhdClF" - }, - "source": [ - "To account for the hemodynamic delay, shift the data by a certain number." - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": { - "id": "-3M-SGu4Un6_" - }, - "outputs": [], - "source": [ - "# Delay chosen based on the TR duration to expect a hemodynamic response\n", - "delay_trials = 2\n", - "\n", - "fmri_figure_data = np.vstack([fmri_run[delay_trials:] for fmri_run in fmri_figure_data])\n", - "stimuli_figure_data = np.vstack([stimuli_run[:-delay_trials] for stimuli_run in stimuli_figure_data]).astype(float)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "uBRznIMLd_F8" - }, - "source": [ - "Flatten the stimuli and verify that inputs and targets are equal in number." - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "KO6mG4LPUn6_", - "outputId": "a0114bfa-3d92-42fd-8fcb-b38f71e879fc" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fmri_figure_data.shape = (1536, 5438)\n", - "stimuli_figure_data.shape = (1536, 100)\n" - ] - } - ], - "source": [ - "# Flatten the stimuli\n", - "\n", - "stimuli_figure_data = np.reshape(stimuli_figure_data, (-1, stimulus_shape[0] * stimulus_shape[1]))\n", - "\n", - "print('fmri_figure_data.shape =',fmri_figure_data.shape) \n", - "print('stimuli_figure_data.shape =',stimuli_figure_data.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ijKHxUiIeKLw" - }, - "source": [ - "### Scoring the test set using the encoding model" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Cj1e2Rkae5Qh" - }, - "source": [ - "Use the trained encoding model to predict for the test simuli.\n", - "\n", - "Consider the actual fmri data extracted for the test set and compute the R2 score." - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": { - "id": "e9ilDKULUn7A" - }, - "outputs": [], - "source": [ - "from sklearn.metrics import r2_score\n", - "\n", - "actual = fmri_figure_data\n", - "predicted = model.predict(stimuli_figure_data)\n", - "\n", - "test_score_map = r2_score(actual, predicted, multioutput='raw_values')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oT942nqege8M" - }, - "source": [ - "Create the thresholded image for plotting." - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": { - "id": "sRb5gdZ9V6fV" - }, - "outputs": [], - "source": [ - "from nilearn.image import threshold_img\n", - "\n", - "# Ignore the negative score values \n", - "test_score_map[test_score_map < 0] = 0\n", - "\n", - "# Bring the scores into the shape of the background brain\n", - "test_score_map_img = masker.inverse_transform(test_score_map)\n", - "\n", - "thresholded_test_score_map_img = threshold_img(test_score_map_img, threshold=1e-6, copy=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 289 - }, - "id": "6wPsSWFBUn7A", - "outputId": "586ba1c8-7553-4a7f-f0bf-740d880e15a4" - }, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from nilearn import image\n", - "from nilearn import plotting\n", - "\n", - "plot = plotting.view_img(thresholded_test_score_map_img, draw_cross=False, symmetric_cmap=False, cmap=plotting.cm.black_red)\n", - "plot" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 559 - }, - "id": "FHDBi04qUn7A", - "outputId": "713cf4e9-4c24-43ad-fb93-4084e72e9b91" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from nilearn.plotting import plot_stat_map\n", - "from nilearn.image import coord_transform\n", - "\n", - "display = plot_stat_map(thresholded_test_score_map_img, bg_img=dataset.background,\n", - " cut_coords=[-8], display_mode='z')\n", - "\n", - "# creating a marker for each voxel and adding it to the statistical map\n", - "\n", - "# Re-set figure size after construction so colorbar gets rescaled too\n", - "fig = plt.gcf()\n", - "fig.set_size_inches(8, 8)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IHvtDizLhADw" - }, - "source": [ - "We can see that the encoding model predicts similar predictability for this unseen stimulus set. \n", - "\n", - "This means that it has learned the stimulus image pixel to brain voxel associations very well and identifies the voxels that are more responsive to the input pixels." - ] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [], - "name": "encoding", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/content/haxby_data.md b/content/haxby_data.md deleted file mode 100644 index 4086e4b..0000000 --- a/content/haxby_data.md +++ /dev/null @@ -1,368 +0,0 @@ ---- -jupytext: - cell_metadata_filter: -all - formats: md:myst - text_representation: - extension: .md - format_name: myst - format_version: 0.13 - jupytext_version: 1.11.5 -kernelspec: - display_name: main_edu_2022 - language: python - name: main_edu_2022 ---- - -(haxby-dataset)= -# An overview of the Haxby dataset - -This part of the `tutorial` aims to make `participants` familiar with the `dataset` we are going to use during this session and also address/introduce/recap some important aspects concerning `datasets` within `machine learning`/`decoding`. The objectives 📍 are: - -- (re-)familiarize everyone with important `datasets` aspects - -- exploring and understand the `tutorial dataset` - - -## A short primer on datasets - -We wanted to avoid "just talking" about `brain decoding` in theory and also showcase how the respective `models` and workflows can be implemented, as well as run to give you some first hands-on experience. Even though we would have loved to get everyone to bring their data and directly apply the things we talk about, it's unfortunately a bit too time-consuming for this setting. Thus, we decided to utilize an `example dataset` that is ready to go and "small enough" to run `decoding models` locally, ie on laptops. You might think "One of those tutorials again...it works with the example dataset but I have little or no chance on running it on/adapting it to my data." and we would agree based on workshops we did ourselves. - -However, we tried our best to address this here by utilizing `software` whose `workflows` and `processing steps` are rather agnostic and implemented via `high-level API` that _should_ allow a comparably straightforward application to different kinds of `data`. This specifically refers to a set of core aspects concerning the dataset's structure and information entailed therein. How about a brief recap? - -```{figure} graphics/decoding_pipeline_example.png ---- -width: 800px -name: decoding_pipeline_example ---- - -A schematic representation of standard `decoding workflow`/`pipeline`. The `input` (`data`) is prepared and potentially `preprocessed` before being submitted to a `model` that then utilizes a certain `metric` to provide a certain `output`. -``` - -Here, we are going to focus on the `input` (`data`). As you heard before, it is usually expected to be structured as `samples` X `features`. - -```{admonition} What could samples X features refer to/entail? -:class: tip, dropdown - -A `sample` could be considered an `observation`/`data point`/one distinct entity in the `dataset`/one distinct part of the `dataset`. For example, if you want to `predict` what a participant perceived based on their `brain activation`/`response`, the `samples` could entail the `fMRI` `scans` or estimated `contrast images` of that `participant`. If you want to `predict` whether a `participant` exhibited a certain `behavior`, e.g. a captured by a `clinical measure`, etc., then the `samples` could comprise different `participants`. - -A `feature` on the other hand would entail/describe certain aspects of a given `sample`. For example, if you want to `predict` what a participant perceived based on their `brain activation`/`response`, the `features` could entail the `voxel pattern` at a certain `ROI` at the given `sample`. -``` - -Thus, in order to make a given `dataset` "ready" for `machine learning`/`decoding`, we need to get it into the respective structure. Lucky for us, the tools we are going to explore, specifically `nilearn`, incorporate this aspect and are make the corresponding process rather easy. What you need to run `machine learning`/`decoding` on your `dataset` is: - -- know what your `samples` are (e.g. `time series`, `statistical maps`, etc.) -- know what your `features` are (e.g. `voxel pattern` of an `ROI`, `annotations`, etc.) -- get the `dataset` in the form `samples` X `features`, ie `samples` are `rows` and `features` are `columns` - -While exploring the `tutorial dataset` we will refer to this to make it more clear. - -```{admonition} Bonus question: ever heard of the "small-n-high-p" (p >> n) problem? -:class: tip, dropdown - -"Classical" `machine learning`/`decoding` models and the underlying algorithms operate on the assumption that are more `predictors` or `features` than there are `sample`. In fact many more. Why is that? -Consider a high-dimensional `space` whose `dimensions` are defined by the number of `features` (e.g. `10 features` would result in a space with `10 dimensions`. The resulting `volume` of this `space` is the amount of `samples` that could be drawn from the `domain` and the number of `samples` entail the `samples` you need to address your `learning problem`, ie `decoding` outcome. That is why folks say: "get more data", `machine learning` is `data`-hungry: our `sample` needs to be as representative of the high-dimensional domain as possible. Thus, as the number of `features` increases, so should the number of `samples` so to capture enough of the `space` for the `decoding model` at hand. - -This referred to as the [curse of dimensionality](https://en.wikipedia.org/wiki/Curse_of_dimensionality) and poses as a major problem in many fields that aim to utilize `machine learning`/`decoding` on unsuitable data. Why is that? -Just imagine we have way more `features` than `samples`, ie `50 features` and `10` `samples`. Instead of having a large amount of `samples` within the `space`, allowing to achieve a sufficient coverage of the latter, we now have a very high-dimensional `space` (`50 dimensions`) and only very few `samples` therein, basically not allowing us to capture nearly enough of the `space` as we would need to. This can result in expected outcomes, misleading results or even lead to complete `model` failure. Furthermore, respective `datasets` often lead to `models` that are `overfitted` and don't `generalize` well. - -However, there are a few things that can be done to address this, including `feature selection`, `projection` into `lower-dimensional` `spaces` or `representations` or `regularization`. - -Question for everyone: what kind have `datasets` do we usually have in `neuroscience`, especially `neuroimaging`? -``` - -+++ - -## Downloading & exploring the `Haxby dataset` - -In the field of `functional magnetic resonance imaging` (`fMRI`), one of the first studies which have demonstrated the feasibility of `brain decoding` was the study by Haxby and colleagues (2001) {cite:p}`Haxby2001-vt`. `Subjects` were presented with various `images` drawn from different `categories` and subsequently a `decoding model` used to `predict` the presented `categories` based on the `brain activity`/`responses`. In the respective parts of this session, we will try to do the same! - -We are going to start with one `subject`, number `4`. To get the `data`, we can simply use [nilearn's dataset module](https://nilearn.github.io/stable/modules/datasets.html). At first, we need to import the respective `module`. - -```{code-cell} ipython3 -import os -from nilearn import datasets -``` - -Next, we get the `data` and going to save it in a directory called `data`. Depending on your machine and internet connection, this might take a minute or so. - -```{code-cell} ipython3 -data_dir = os.path.join('..', 'data') -haxby_dataset = datasets.fetch_haxby(subjects=[4], fetch_stimuli=True, data_dir=data_dir) -``` - -What do we have now? Lets have a look! - -```{code-cell} ipython3 -haxby_dataset -``` - -As you can see, we get a `python dictionary` and there's quite bit in it. This includes: - -- the `anatomical data` under `anat` -- the `functional data` under `func` -- an annotation when `participants` perceived what `category` -- several `masks` under `mask*` -- a `dataset` `description` -- `stimuli categories` and respective `stimuli` - -+++ - -`````{admonition} Thinking about input data again... -:class: tip -What would be our `samples` and `features`? -````` - -+++ - -## The data in more depth - -After getting a first idea of what our `dataset` entails, we should spend a bit more time exploring it in more depth, starting with the neuroimaging files. - -### Neuroimaging files - -As seen above, the data includes several `nii` files, which contain `images` of `brain volumes`, either `anatomical` or `functional` `scans`, as well as (`binary`) `masks`. Lets have a look at the `anatomical` `image` first. -Using `nilearn`, we can either `load` and then `plot` it or directly `plot` it. Here we are going to do the first option as it will allow us to check the properties of the `image`. - -```{code-cell} ipython3 -from nilearn.image import load_img -``` - -```{code-cell} ipython3 -anat_image = load_img(haxby_dataset.anat) -``` - -Now we can access basically all parts of the `image`, including the `header` - -```{code-cell} ipython3 -print(anat_image.header) -``` - -and actual `data`. - -```{code-cell} ipython3 -anat_image.dataobj -``` - -```{code-cell} ipython3 -anat_image.dataobj.shape -``` - -As you can see, this is basically a `numpy array` that has the same `dimensions` as our `image` and the `data` reflect `values` for a given `voxel`. So far so good but how does it actually look? We can make use of one of `nilearn`'s many [plotting functions](https://nilearn.github.io/stable/modules/plotting.html). - -```{code-cell} ipython3 -from nilearn import plotting -``` - -```{code-cell} ipython3 -plotting.plot_anat(anat_image) -``` - -We can even create an `interactive plot`: - -```{code-cell} ipython3 -plotting.view_img(anat_image, symmetric_cmap=False, cmap='Greys_r', colorbar=False) -``` - -Comparably, we can do the same things with the `functional` `image`. That is `load`ing the `image`: - -```{code-cell} ipython3 -func_image = load_img(haxby_dataset.func) -``` - -and inspect its `header`: - -```{code-cell} ipython3 -print(func_image.header) -``` - -and `data`: - -```{code-cell} ipython3 -func_image.get_data() -``` - -```{code-cell} ipython3 -func_image.dataobj.shape -``` - -`````{admonition} We already noticed something... -:class: tip -The `data` of the `anatomical` and `functional` `image` are quite different. Do you know why and which we would use for our planned `decoding` analyses? -````` - -+++ - -As we have a `4D` `image`, that is `brain volumes` acquired over time (the `4th dimension`), we need to adapt the `plotting` a bit. More precisely, we need to either `plot` a `3D image` at a given `time point` or e.g. compute the `mean image` over `time` and `plot` that. The latter might be more informative and additional shows you how easy this can be done using `nilearn`'s [image functions](https://nilearn.github.io/stable/modules/image.html). Thus, we, at first, `import` the respective `function` and compute the `mean image`: - -```{code-cell} ipython3 -from nilearn.image import mean_img -``` - -```{code-cell} ipython3 -func_image_mean = mean_img(func_image) -``` - -We can check if this worked via the approach we followed above, ie checking the `data`: - -```{code-cell} ipython3 -func_image_mean.dataobj.shape -``` - -That seems about right and we can give the plot a try: - -```{code-cell} ipython3 -plotting.plot_epi(func_image_mean, cmap='magma') -``` - -and of course, this also works for `interactive` plots. - -```{code-cell} ipython3 -plotting.view_img(func_image_mean, cmap='magma', symmetric_cmap=False) -``` - -The last type of `neuroimaging` file we need to check are the (`binary`) `masks`, so let's do it for one example `mask`: the `ventral temporal cortex`. This mask has been generated as part of the Haxby et al. (2001) study {cite:p}`Haxby2001-vt`, and highlights a part of the brain specialized in the processing of visual information, and which contains areas sensitive to different types of image categories {cite:p}`grill-spector_functional_2014` . As with the types before, we can `load`, - -```{code-cell} ipython3 -vt_mask = load_img(haxby_dataset.mask_vt) -``` - -`inspect` - -```{code-cell} ipython3 -print(vt_mask.header) -``` - -```{code-cell} ipython3 -vt_mask.get_data() -``` - -```{code-cell} ipython3 -vt_mask.dataobj.shape -``` - -and `visualize` it (Here, we are going to plot it as an overlay on the `anatomical image`). - -```{code-cell} ipython3 -plotting.plot_roi(vt_mask, bg_img=anat_image, - cmap='Paired') -``` - -With that, we had a quick look at all `neuroimaging` `file` `types` present in the `dataset` and can continue to have a look at the other `file types` (and information therein) required to apply a `decoding model`. - -+++ - -### Labels and stimulus annotations - -As mentioned in prior sessions (e.g.[Supervised learning using scikit-learn](https://main-educational.github.io/material.html#supervised-learning-using-scikit-learn) and hinted at the [beginning of this session](#A-short-primer-on-datasets), when working on a `supervised learning problem`, we also need the `ground truth`/`true labels` for each `sample`. Why? Because we need to evaluate how a given `model` performs via comparing the `labels` it `predicted` to the `true labels`. What these `labels` refer to can be manifold and of course depends on the `task` at hand. - -For example, a `supervised learning problem` in the `dataset` at hand could entail `training` a `model` to `recognize` and `predict` what `category` `participants` perceived based on their `brain activation`. Thus, we would need to know what `category` was shown when during the acquisition of the `data` (or which `category` resulted in which `estimated` `brain activity`). - -Within our `tutorial dataset`, this information is included in the `session_target` file. Using [pandas](https://pandas.pydata.org/pandas-docs/stable/index.html) we can easily `load` and `inspect` this file: - -```{code-cell} ipython3 -import pandas as pd -stimulus_annotations = pd.read_csv(haxby_dataset.session_target[0], delimiter=' ') -``` - -```{code-cell} ipython3 -stimulus_annotations.head(n=40) -``` - -While this is already informative, let's plot it to get a better intuition. - -```{code-cell} ipython3 -import seaborn as sns -import matplotlib.pyplot as plt - -ax = sns.scatterplot(x=stimulus_annotations.index, y=stimulus_annotations['labels'], - hue=stimulus_annotations['labels'], legend=False, palette='colorblind') -plt.title('Categories shown across time') -ax.set_xlabel('Time point/fMRI scan') -sns.despine(offset=5) -``` - -As we can see, the information provided indicates what `category` `participants` perceived at which `sample` or `fMRI image acquisition` aka point in time during the experiment. With that, we have the needed `labels` for our `samples` (ie our `Y`) and can thus apply a `supervised learning problem`. - -+++ - -## Summary - -This already concludes this section of the session within which we explored the went through basic `datasets` concepts again and afterwards explored the `tutorial dataset` which we are going to use during the remaining sections of this session, ie [Decoding via SVM](), [Decoding using MLPs]() and [Decoding using GCNs](). - -Within this section you should have learned: - -- important aspects of `datasets` - - structured input in the form `samples X features` - - `small n high p` problem - - -- the `tutorial dataset` - - background - - file types and information therein - - `neuroimaging` files - - `stimulus annotations` - -If you have any questions, please don't hesitate to ask us. Thank you very much for your attention and see you in the next section. - -+++ - -## References - -```{bibliography} -:filter: docname in docnames -``` - -+++ - -+++ - -## Bonus: checking the stimuli - -As you saw above, our `tutorial dataset` actually also contains the `stimuli` utilized in the experiment. This pretty unique (because of e.g. copyright problems) but really cool. As we could use the `stimuli` for certain analyses, e.g. [encoding]() and/or comparing their processing in `biological` and `artificial neural networks`. However, this is unfortunately outside the scope of this session. Thus, we are just going to plot a few of them so you get an impression. - -We can examine one functional volume using nilearn's plotting tools. Because fmri data are 4D we use [nilearn.image.mean_img](https://nilearn.github.io/modules/generated/nilearn.image.mean_img.html#nilearn.image.mean_img) to extract the average brain volume. - -```{code-cell} ipython3 -import matplotlib.pyplot as plt - -from nilearn import datasets -from nilearn.plotting import show - -stimulus_information = haxby_dataset.stimuli - -for stim_type in stimulus_information: - # skip control images, there are too many - if stim_type != 'controls': - - file_names = stimulus_information[stim_type] - file_names = file_names[0:16] - fig, axes = plt.subplots(4, 4) - fig.suptitle(stim_type) - - for img_path, ax in zip(file_names, axes.ravel()): - ax.imshow(plt.imread(img_path), cmap=plt.cm.gray) - - for ax in axes.ravel(): - ax.axis("off") - -show() -``` - -Please note that for each `image` `category`, a number of `scrambled images` were also presented. - -```{code-cell} ipython3 -for stim_num in range(len(stimulus_information['controls'])): - stim_type = stimulus_information['controls'][stim_num][0] - file_names = stimulus_information['controls'][stim_num][1] - file_names = file_names[0:16] - fig, axes = plt.subplots(4, 4) - fig.suptitle(stim_type) - - for img_path, ax in zip(file_names, axes.ravel()): - ax.imshow(plt.imread(img_path), cmap=plt.cm.gray) - - for ax in axes.ravel(): - ax.axis("off") - -show() -```