{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Predicting individual differnces with Connectome embeddings\n", "\n", "Producing individual-level CE requires learning a compact vectorized representation of\n", " brain nodes and aligning the representations obtained from different individuals to\n", " the same latent space. Here we will demonstrate CE ability to preserve variance\n", " associated with age and to improve age estimation compare to the raw structural\n", " connectivity.\n", "\n", "\n", "We focus on age here since it is a variable with strong relation to brain connectivity\n", "(Faskowitz, Yan, Zuo, & Sporns, 2018), widespread effects on cognition and is openly\n", "available within The Enhanced Nathan Kline Institute-Rockland Sample\n", "(Nooner et al., 2012; http://fcon_1000.projects.nitrc.org/indi/enhanced/neurodata.html).\n", " In the paper we also estimate intelligence from CE, but this data requires an access\n", " request and cannot be shared here.\n", "\n", "\n", "\n", "\"CE\n", "\n", "\n", "First, lets import some relevant packages:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "%%capture\n", "!pip install -U scikit-learn\n", "!pip install seaborn\n", "%matplotlib inline\n", "from matplotlib import pyplot as plt\n", "from matplotlib.colors import rgb2hex\n", "import numpy as np\n", "import seaborn as sns\n", "import pandas as pd\n", "from scipy import stats\n", "from sklearn.metrics.pairwise import cosine_similarity\n", "from sklearn.preprocessing import scale\n", "from sklearn.model_selection import KFold\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.linear_model import SGDRegressor\n", "from sklearn.base import clone\n", "from sklearn.metrics import explained_variance_score\n", "from tqdm import tqdm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Download the relevant data:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "%%capture\n", "# aligned CEs (from the previous notebook\n", "!wget -O NKI_200_schaefer_subjects_ce.npz 'https://github.com/GidLev/cepy/blob/master/examples/data/NKI_200_schaefer_subjects_ce.npz?raw=true';\n", "ces_subjects = np.load('NKI_200_schaefer_subjects_ce.npz')['x']\n", "print(ces_subjects.shape)\n", "\n", "# structural connectivity matrices\n", "!wget -O NKI_200_schaefer_sc_matrices.npz 'https://github.com/GidLev/cepy/blob/master/examples/data/NKI_200_schaefer_sc_matrices.npz?raw=true';\n", "sc_matrices = np.load('NKI_200_schaefer_sc_matrices.npz')['matrices']\n", "print(sc_matrices.shape)\n", "\n", "# subject labels\n", "!wget -O NKI_demographics.csv 'https://github.com/GidLev/cepy/blob/master/examples/data/NKI_demographics.csv?raw=true';\n", "subject_labels = pd.read_csv('NKI_demographics.csv')\n", "print(subject_labels.head(10))\n", "subjects_test_bool = subject_labels['test_set']\n", "subjects_ages = subject_labels['Calculated_Age']\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read node labels:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "%%capture\n", "# download\n", "!wget -O schaefer200_yeo17_networks.csv 'https://github.com/GidLev/cepy/blob/master/examples/data/schaefer200_yeo17_networks.csv?raw=true';\n", "\n", "# read into a dataframe\n", "node_labels = pd.read_csv('schaefer200_yeo17_networks.csv', index_col = 0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate the cosine similarity among all embeddings vector pairs, summarized as the cosine matrix:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "cosine_matrices = np.zeros((ces_subjects.shape[0], ces_subjects.shape[1], ces_subjects.shape[1]))\n", "\n", "for subject_i in np.arange(ces_subjects.shape[0]):\n", " cosine_matrices[subject_i,...] = cosine_similarity(ces_subjects[subject_i,...])" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "We have three features types (inputs to the prediction model) to compare: \n", "\n", "* The raw structural connectivity (SC)\n", "\n", "* The cosine similarity matrix (CM)\n", "\n", "* The connectome embedding (CE)\n", "\n", "Within the training set, we first construct a separate model for each node and compare across the three feature types:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "num_nodes = ces_subjects.shape[1]\n", "input_names = ['CE', 'Cosine matrix', 'Structural connectivity']\n", "\n", "# create dataframe to store the results\n", "df_ce, df_cm, df_sc = node_labels.copy(), node_labels.copy(), node_labels.copy()\n", "df_ce['input_type'], df_cm['input_type'], df_sc['input_type'] = input_names\n", "dfs = [df_ce, df_cm, df_sc]\n", "\n", "# define the features (CE, CE cosine matrix, SC matrix) within the training set\n", "x_ce = ces_subjects[~subjects_test_bool,...].copy()\n", "x_cm = cosine_matrices[~subjects_test_bool,...].copy()\n", "x_sc = sc_matrices[~subjects_test_bool,...].copy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we normalize the age values and initiate an empty array to store the predictions:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# and the real and predicted labela - age\n", "y = subjects_ages[~subjects_test_bool].values\n", "y_mean, y_std = y.mean(), y.std()\n", "y = (y - y_mean) / y_std\n", "y_pred_cv = np.zeros((len(input_names), len(y), num_nodes))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can start training the model using 5-fold cross validation. Notice this part is time consuming,\n", "consider changing *n_splits* to 3. Training on a subset of nodes will also reduce execution time:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 200/200 [01:05<00:00, 3.05it/s]\n" ] } ], "source": [ "kf = KFold(n_splits=5)\n", "lm = LinearRegression() # we use a simple linear regression\n", "\n", "for node_i in tqdm(np.arange(num_nodes)): # loop over all nodes\n", " # a boolean vector to remove the diagonal as a feature\n", " exclude_diag = np.ones(num_nodes, dtype=bool)\n", " exclude_diag[node_i] = False\n", "\n", " inputs = [x_ce[:,node_i,:], x_cm[:,node_i,exclude_diag], x_sc[:,node_i,exclude_diag]] # select a node\n", " inputs = [scale(x) for x in inputs] # standardize the inputs (mean = 0, std = 1)\n", " \n", " for input_i, (input_name, input_x, input_df) in enumerate(zip(input_names, inputs, dfs)): # loop over the three input types\n", " for ind_kf, (train_index, test_index) in enumerate(kf.split(input_x)): # the 5-fold iterator\n", " curr_lm = clone(lm)\n", " curr_lm.fit(input_x[train_index, :], y[train_index])\n", " y_pred_cv[input_i, test_index, node_i] = curr_lm.predict(input_x[test_index, :])\n", " \n", " # keep the pearson correlation coefficient between the real and predicted age \n", " input_df.loc[node_i, 'correlation_coef'], _ = stats.pearsonr(y, y_pred_cv[input_i, :, node_i])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the results:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "input_dfs = pd.concat(dfs) # concatanate all results dataframes\n", "\n", "\n", "# define a color palette\n", "colors_viridis = [[0.1359, 0.2414, 0.4251],\n", " [0.8287, 0.7568, 0.3961],\n", " [0.4861, 0.4826, 0.4707]]\n", "\n", "c_palette = {'CE': rgb2hex(colors_viridis[0]), \n", " 'Cosine matrix': rgb2hex(colors_viridis[1]), \n", " 'Structural connectivity': rgb2hex(colors_viridis[2])}\n", "\n", "# plot a joint swarm and box plot\n", "sns.set_style(\"white\")\n", "fig, ax = plt.subplots(figsize=(16, 8)) \n", "sns.swarmplot(x=\"Yeo_7_nets\", y='correlation_coef', hue = 'input_type', data=input_dfs, dodge=True,\n", " hue_order = input_names, palette=c_palette, size = 5, ax=ax)\n", "sns.boxplot(x=\"Yeo_7_nets\", y='correlation_coef', hue = 'input_type', data=input_dfs, hue_order = input_names,\n", " showcaps=True, boxprops={'facecolor': 'None'},\n", " showfliers=False, whiskerprops={'linewidth': 2}, ax=ax)\n", "plt.xlabel(\"7 canonical resting-state networks\", fontsize=20)\n", "plt.ylabel('Observed-predicted correlation', fontsize=20)\n", "ax.tick_params(labelsize=18)\n", "\n", "# add a legend\n", "ax.get_legend().remove()\n", "handles, labels = ax.get_legend_handles_labels()\n", "plt.legend(handles[3:], labels[3:], fontsize = 14, loc = 2);\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's train a model based on all nodes and test its performance on the left-out test set. As in the paper this would be conducted in two steps:\n", "\n", "\n", "* First level models as we already trained on individual edges\n", "\n", "* whole connectome, second-level models were created by training an ensemble of the nodal level predictions\n", "\n", "Again we start by preparing the features and labels:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# initiate the prediction models\n", "lm_first = LinearRegression()\n", "lm_second = SGDRegressor(random_state = 1)\n", "\n", "# define the features (SC matrix, CE cosine matrix, CE)\n", "x_test_sc = sc_matrices[subjects_test_bool,...].copy()\n", "x_test_cm = cosine_matrices[subjects_test_bool,...].copy()\n", "x_test_ce = ces_subjects[subjects_test_bool,...].copy()\n", "\n", "# get the real and predicted labels of the test set\n", "y_test = subjects_ages[subjects_test_bool].values\n", "y_test = (y_test - y_mean) / y_std\n", "y_test_first_level = np.zeros((len(input_names), len(y_test), num_nodes))# and for the training set (without the cross validation)\n", "y_test_pred = np.zeros((len(input_names), len(y_test)))\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Train the nodal, first-level models. Notice this part is time consuming. Training on a subset of nodes can reduce execution time:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 200/200 [00:17<00:00, 11.22it/s]\n" ] } ], "source": [ "for node_i in tqdm(np.arange(num_nodes)): # loop over all nodes\n", " # a boolean vector to remove the diagonal as a feature\n", " exclude_diag = np.ones(num_nodes, dtype=bool)\n", " exclude_diag[node_i] = False\n", "\n", " inputs_test = [x_test_ce[:,node_i,:], x_test_cm[:,node_i,exclude_diag], x_test_sc[:,node_i,exclude_diag]] # select node\n", " inputs_test = [scale(x) for x in inputs_test] # standartize the input (mean = 0, std = 1)\n", " \n", " inputs = [x_ce[:,node_i,:], x_cm[:,node_i,exclude_diag], x_sc[:,node_i,exclude_diag]] # select node\n", " inputs = [scale(x) for x in inputs] # standartize the input (mean = 0, std = 1)\n", " \n", " for input_i, (input_train, input_test) in enumerate(zip(inputs, inputs_test)): # loop over the three input types\n", " curr_lm = clone(lm_first)\n", " curr_lm.fit(input_train, y)\n", " y_test_first_level[input_i, :, node_i] = curr_lm.predict(input_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And the whole connectome, second-level models: " ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# create array to store the results\n", "second_level_results_corr = [0] * 3 \n", "second_level_results_ev = [0] * 3 \n", "\n", "\n", "# now train the second-level model on the cross validated first-level results and produce predictions for the test set\n", "for input_i in np.arange(len(input_names)): # loop over the three input types\n", " curr_lm = clone(lm_second)\n", " curr_lm.fit(y_pred_cv[input_i, ...], y)\n", " y_test_pred[input_i, :] = curr_lm.predict(y_test_first_level[input_i, ...])\n", " \n", " second_level_results_corr[input_i], _ = stats.pearsonr(y_test, y_test_pred[input_i, :])\n", " second_level_results_ev[input_i] = explained_variance_score(y_test, y_test_pred[input_i, :])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we plot the performance metrics obtained on the untouched test-set:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Connectome embedding model explained variance : 0.579\n", "Cosine similarity model explained variance : 0.505\n", "Structural connectivity model explained variance : -1.85e+75\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set_style(\"white\")\n", "fig, ax = plt.subplots(figsize=(12, 8)) \n", "\n", "sns.pointplot(x=input_names, y=second_level_results_corr,ax=ax,\n", " palette=c_palette, size = 10)\n", "\n", "ax.tick_params(labelsize = 20)\n", "plt.xlabel(\"\", fontsize=20)\n", "plt.ylabel('Observed-predicted correlation', fontsize=20);\n", "\n", "print('Connectome embedding model explained variance : {:.3}'.format(second_level_results_ev[0]))\n", "print('Cosine similarity model explained variance : {:.3}'.format(second_level_results_ev[1]))\n", "print('Structural connectivity model explained variance : {:.3}'.format(second_level_results_ev[2]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Referece\n", "\n", "* Faskowitz, J., Yan, X., Zuo, X. N., & Sporns, O. (2018). Weighted Stochastic Block Models of the Human Connectome across the Life Span. Scientific Reports, 8(1), 12997. https://doi.org/10.1038/s41598-018-31202-1\n", "\n", "* Nooner, K. B., Colcombe, S. J., Tobe, R. H., Mennes, M., Benedict, M. M., Moreno, A. L., … Milham, M. P. (2012). The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry. Frontiers in Neuroscience, 6, 152. https://doi.org/10.3389/fnins.2012.00152" ] } ], "metadata": { "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": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 1 }