{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Parameter exploration of a brain network model\n", "\n", "This notebook demonstrates how to scan the parameter space of a brain network model using `neurolib`. We will simulate BOLD activity and compare the results to empirical data to identify optimal parameters of the model.\n", "\n", "The steps outlined in this notebook are the following:\n", "\n", "1. We load a DTI and resting-state fMRI dataset (`hcp`) and set up a brain network using the `FHNModel`.\n", "2. We simulate the system for a range of different parameter configurations.\n", "3. We load the simulated data from disk. \n", "4. We postprocess the results and obtain the model fit.\n", "5. Finally, we plot the results in the parameter space of the exploration." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "source": [ "#hide\n", "# change to the root directory of the project\n", "import os\n", "if os.getcwd().split(\"/\")[-1] == \"examples\":\n", " os.chdir('..')\n", " \n", "# This will reload all imports as soon as the code changes\n", "%load_ext autoreload\n", "%autoreload 2 " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#hide\n", "import logging\n", "logging.getLogger().setLevel(logging.INFO)\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#hide\n", "try:\n", " import matplotlib.pyplot as plt\n", "except ImportError:\n", " import sys\n", " !{sys.executable} -m pip install matplotlib\n", " import matplotlib.pyplot as plt\n", " \n", "import numpy as np\n", "\n", "# Let's import all the necessary functions for the parameter\n", "from neurolib.models.fhn import FHNModel\n", "from neurolib.utils.parameterSpace import ParameterSpace\n", "from neurolib.optimize.exploration import BoxSearch\n", "\n", "# load some utilty functions for explorations\n", "import neurolib.utils.pypetUtils as pu\n", "import neurolib.utils.paths as paths\n", "import neurolib.optimize.exploration.explorationUtils as eu\n", "\n", "# The brain network dataset\n", "from neurolib.utils.loadData import Dataset\n", "\n", "# Some useful functions are provided here\n", "import neurolib.utils.functions as func\n", "\n", "# a nice color map\n", "plt.rcParams['image.cmap'] = 'plasma'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Set up a brain network" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We load a dataset (in this case the `hcp` dataset from the Human Connectome Project) and initialize a model to run on each node of the brain network (here the `FHNModel` which is the Fitz-Hugh Nagumo model)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ds = Dataset(\"hcp\")\n", "model = FHNModel(Cmat = ds.Cmat, Dmat = ds.Dmat)\n", "model.params.duration = 20 * 1000 #ms\n", "# testing: model.params.duration = 20 * 1000 #ms\n", "# original: model.params.duration = 5 * 60 * 1000 #ms" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Running the model is as simple as entering `model.run(chunkwise=True)`. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Run the exploration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We define a parameter range to explore. Our first parameter is `x_ext`, which is the input to each node of the `FHNModel` in a brain network. Therefore, this parameter is a `list` with `N` entries, one per node. Our next parameter is `K_gl`, the global coupling strength. Finally, we have the `coupling` parameter, which defines how each `FHNModel` is coupled to its adjacent nodes via either `additive` coupling (`activity += input`) or `diffusive` (`activity += (activity - input)` )." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "parameters = ParameterSpace({\"x_ext\": [np.ones((model.params['N'],)) * a for a in np.linspace(0, 2, 2)] # testing: 2, original: 41\n", " ,\"K_gl\": np.linspace(0, 2, 2) # testing: 2, original: 41\n", " ,\"coupling\" : [\"additive\", \"diffusive\"]\n", " }, kind=\"grid\")\n", "search = BoxSearch(model=model, parameterSpace=parameters, filename=\"example-1.2.0.hdf\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We run the exploration, simply by calling the `run()` function of the `BoxSearch` class. We can pass parameters to this function, that will be directly passed to the `FHNModel.run()` function of the simulated model. This way, we can easily specify to run the simulation `chunkwise`, without storing all the activity in memory, and simulate `bold` activity as well. \n", "\n", "Note that the default behaviour of the `BoxSearch` class is to save the `default_output` of each model and if `bold` is simulated, then also the BOLD data. If the exploration is initialized with `BoxSearch(saveAllModelOutputs=True)`, the exploration would save *all* outputs of the model. This can obviously create a lot of data to store, so please use this option at your own discretion. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "search.run(chunkwise=True, bold=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Load results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A simple helper function for getting the trajectories of an `hdf` file created by `pypet` can be found in `pypetUtils.py` (aka `pu`). This way, you can explore which explorations are in the file and decide later which one you want to load for analysis" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['results-2020-04-08-02H-01M-53S', 'results-2020-04-08-02H-50M-09S']" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pu.getTrajectorynamesInFile(os.path.join(paths.HDF_DIR, \"example-1.2.0.hdf\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default behaviour will load the latest exploration. It's name is also stored in `search.trajectoryName`:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'results-2020-04-08-02H-50M-09S'" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "search.trajectoryName" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we load all results. As said above, the newest exploration will be loaded by default. You can load results from earlier explorations by adding the argument `trajectoryName=results-from-earlier` and also chose another `hdf` file by using the argument `filename=/path/to/explorations.hdf`.\n", "\n", "Remember that using `search.loadResults()` will load all results to memory. This can cause a lot of RAM, depending on how big the exploration was. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "search.loadResults()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(\"Number of results: {}\".format(len(search.results)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One way of loading a result without loading everything else into RAM is to use the builtin function `search.getRun()`. However, you need to know which `runId` you're looking for! For this, you can run `search.loadDfResults()` to create a pandas.DataFrame `search.dfResults` with all parameters (which also happens when you call `search.loadResults()`)." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'x_ext': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),\n", " 'K_gl': 0.15000000000000002,\n", " 'coupling': 'additive'}" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "search.getRun(6).params" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After loading the results with `search.loadResults()` they are now available as a simple list using `search.results`. Let's look at the time series of one result." ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Activity')" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "rId = 2 # test:2, original: 1327\n", "plt.plot(search.results[rId].t, search.results[rId].x.T);\n", "plt.xlabel(\"Time [ms]\")\n", "plt.ylabel(\"Activity\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using `search.loadResults()` also created a `pandas.DataFrame` with the individual run's parameters and their `runId`." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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x_extK_glcouplingfcmax_xamp_x
33582.01.95additive0.3044962.4462071.463651e+00
33592.01.95diffusive0.2212380.8721102.275957e-14
33602.02.00additive0.3103892.4892081.503437e+00
33612.02.00diffusive0.2267290.8721102.253753e-14
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" ], "text/plain": [ " x_ext K_gl coupling fc max_x amp_x\n", "3358 2.0 1.95 additive 0.304496 2.446207 1.463651e+00\n", "3359 2.0 1.95 diffusive 0.221238 0.872110 2.275957e-14\n", "3360 2.0 2.00 additive 0.310389 2.489208 1.503437e+00\n", "3361 2.0 2.00 diffusive 0.226729 0.872110 2.253753e-14" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "search.dfResults.iloc[-4:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you remember from before, the external input parameter `x_ext` is a `list` of length `N` (one per node). Since they're all the same in this example, we reduce the parameter to only the first entry of each list." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "search.dfResults.x_ext = [a[0] for a in list(search.dfResults.x_ext)]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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x_extK_glcoupling
33582.01.95additive
33592.01.95diffusive
33602.02.00additive
33612.02.00diffusive
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" ], "text/plain": [ " x_ext K_gl coupling\n", "3358 2.0 1.95 additive\n", "3359 2.0 1.95 diffusive\n", "3360 2.0 2.00 additive\n", "3361 2.0 2.00 diffusive" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "search.dfResults.iloc[-4:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. Postprocessing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use `eu.processExplorationResults()` from `explorationUtils.py` (aka `eu`) to process the results from the simluation and store results in our `pandas.DataFrame` of all results called `search.dfResults`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "eu.processExplorationResults(search, model=model, ds=ds, bold_transient=10000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This finally gives us a dataframe with parameters and respective values from postprocessing the results, which we can access using `search.dfResults`.\n", "\n", "We can use the utility function `eu.findCloseResults()` to navigate in this DataFrame and find for example the `runId` of a run for a specific parameter configuration." ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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x_extK_glcouplingfcmax_xamp_x
13240.800.30additive0.3649101.1922671.428502
13250.800.30diffusive0.3024870.5767650.467873
13260.800.35additive0.3372261.2416131.511995
13270.800.35diffusive0.1872380.5479170.423548
13280.800.40additive0.2004891.2876261.590182
.....................
19091.150.55diffusive0.3638090.7726980.577180
19101.150.60additive0.3489881.2342061.050313
19111.150.60diffusive0.2781030.7688220.566546
19121.150.65additive0.3719431.2769291.091328
19131.150.65diffusive0.2929930.7623550.550818
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128 rows × 6 columns

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" ], "text/plain": [ " x_ext K_gl coupling fc max_x amp_x\n", "1324 0.80 0.30 additive 0.364910 1.192267 1.428502\n", "1325 0.80 0.30 diffusive 0.302487 0.576765 0.467873\n", "1326 0.80 0.35 additive 0.337226 1.241613 1.511995\n", "1327 0.80 0.35 diffusive 0.187238 0.547917 0.423548\n", "1328 0.80 0.40 additive 0.200489 1.287626 1.590182\n", "... ... ... ... ... ... ...\n", "1909 1.15 0.55 diffusive 0.363809 0.772698 0.577180\n", "1910 1.15 0.60 additive 0.348988 1.234206 1.050313\n", "1911 1.15 0.60 diffusive 0.278103 0.768822 0.566546\n", "1912 1.15 0.65 additive 0.371943 1.276929 1.091328\n", "1913 1.15 0.65 diffusive 0.292993 0.762355 0.550818\n", "\n", "[128 rows x 6 columns]" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eu.findCloseResults(search.dfResults, dist=0.2, K_gl=0.5, x_ext = 1.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To understand what is happening in `eu.processExplorationResults()`, it helps to see how we could do postprocessing on the loaded data ourselves. Let's calculate the correlation to empirical functional connectivity using the builtin funtions `func.fc()` and `func.matrix_correlation()`." ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean correlation of run 3324 with empirical FC matrices is 0.28\n" ] } ], "source": [ "mean_corr = np.mean([func.matrix_correlation(func.fc(search.results[rId]['BOLD']), fc) for fc in ds.FCs])\n", "\n", "print(f\"Mean correlation of run {rId} with empirical FC matrices is {mean_corr:.02}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 5. Plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another usefull function is `eu.plotExplorationResults()`, which helps you to visualize the results from the exploration. You can specify which parameters should be the x- and the y-axis using the `par1=[parameter_name, parameter_label]` and `par2` arguments, and you can define `by` which paramter plane the results should be \"sliced\"." ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_key_label = \"Maximum of output\"\n", "eu.plotExplorationResults(search.dfResults, par1=['x_ext', '$x_{ext}$'], par2=['K_gl', '$K$'], plot_key='max_x', by=['coupling'], by_label = ['coupling'], plot_key_label=plot_key_label, one_figure=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## BOLD functional connectivity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want to find parameter for which the brain network model produces realistic BOLD functional connectivity. For this, we calculated the entry `fc` in `search.dfResults` by taking the `func.fc()` of the `model.BOLD` timeseries and compared it to empirical data using `func.matrix_correlation`. \n", "\n", "Below, the average of this value across all subjects of the dataset is plotted. A higher value (brighter color) means a better fit to the empirical data. Observe how the best solutions tend to cluster at the edges of bifurcations, indicating that correlations in the network are generated by multiple nodes undergoing bifurcation together, such as transitioning from the constant activity (fixed point) solution to an oscillation." ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_key_label = \"FC correlation\"\n", "eu.plotExplorationResults(search.dfResults, par1=['x_ext', '$x_{ext}$'], par2=['K_gl', '$K$'], plot_key='fc', by=['coupling'], by_label = ['coupling'], plot_key_label=plot_key_label, one_figure=True)" ] } ], "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": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 4 }