{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Across-Sessions Within Subject Testing with [GLHMM toolbox](https://github.com/vidaurre/glhmm)\n", "\n", "In this tutorial, we explore how to implement the Across-sessions-test-within subjects using the [GLHMM toolbox](https://github.com/vidaurre/glhmm), part of the paper [The Gaussian-Linear Hidden Markov Model: a Python Package](https://direct.mit.edu/imag/article/doi/10.1162/imag_a_00460/127499/The-Gaussian-Linear-Hidden-Markov-model-a-Python). This test is ideal for studying variability across different sessions, making it particularly suited for longitudinal studies involving multiple scanning sessions.\n", "\n", "In real-world applications, one would typically fit a Hidden Markov Model (HMM) to actual data. However, for demonstration purposes, we use [synthetic data](https://github.com/vidaurre/glhmm/tree/main/docs/notebooks/data_statistical_testing) for both the brain data ($D$) and the behavioural variable ($R$) in the ```across_sessions_within_subject``` test.\n", "\n", "The synthetic data is generated using the toolbox [Genephys](https://github.com/vidaurre/genephys), developed by [Vidaurre in 2023](https://elifesciences.org/reviewed-preprints/87729). Genephys enables the simulation of electrophysiological data in psychometric experiments, creating scenarios where a subject is exposed to various stimuli while EEG or MEG data is recorded.\n", "\n", "For this tutorial:\n", "\n", "* The simulated brain data ($D$) consists of EEG or MEG recordings with 16 channels, measured across multiple trials and sessions. Each trial consists of 250 timepoints.\n", "* The behavioural data ($R$) is continuous and represents reaction times for individual trials.\n", "\n", "While data preparation requires some explanation, running the ```across_sessions_within_subject``` test is straightforward. Simply provide the input variables ($D$ and $R$) and specify the method for the analysis.\n", "\n", "**Note:** Due to rendering issues when viewing this notebook through github, internal links, like the table of contents, may not work correctly. To ensure that the notebook renders correctly, you can view it through [this link](https://nbviewer.org/github/vidaurre/glhmm/blob/main/docs/notebooks/Testing_across_sessions_within_subject.ipynb).\n", "\n", "Authors: Nick Yao Larsen \n", "\n", "## Table of Contents\n", "1. [Load and prepare data](#load-data)\n", " * [Look at data](#look-data)\n", " * [Prepare data for HMM](#prep_hmm)\n", "2. [Load data or initialise and train HMM](#load_hmm)\n", " * [Data restructuring](#data-recon)\n", "3. [Across-sessions within subject testing](#across_sessions)\n", " * [Across-sessions within subject testing - Multivariate](#perm-regression)\n", " * [Across-sessions within subject testing - Univariate](#perm-correlation)\n", " * [Across-trials - Multivariate](#perm-regression-trial)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Install necessary packages\n", "If you dont have the **GLHMM-package** installed, then run the following command in your terminal:\n", "\n", "```pip install git+https://github.com/vidaurre/glhmm```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import libraries\n", "Let's start by importing the required libraries and modules." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from pathlib import Path\n", "from glhmm import glhmm, graphics, statistics,preproc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load and prepare data \n", "First, we'll load the synthetic data from this [folder](https://github.com/vidaurre/glhmm/tree/main/docs/notebooks/data_statistical_testing) and use the [glhmm toolbox](https://github.com/vidaurre/glhmm) to train a classic HMM on the synthetic data that represents EEG or MEG measurements.\\\n", "Let's start by loading the essential data for this tutorial:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data dimension of D-session data: (250, 1500, 16)\n", "Data dimension of R-session data: (1500,)\n", "Data dimension of indices: (10, 2)\n" ] } ], "source": [ "# Get the current directory\n", "PATH_PARENT = Path.cwd()\n", "# Path of the location of the data\n", "PATH_DATA = PATH_PARENT / \"data_statistical_testing\"\n", "\n", "# Load D data\n", "D_sessions = np.load(PATH_DATA/\"D_sessions.npy\")\n", "# Load R data\n", "R_sessions = np.load(PATH_DATA/\"R_sessions.npy\")\n", "# Load indices\n", "idx_sessions = np.load(PATH_DATA/\"idx_sessions.npy\")\n", "\n", "print(f\"Data dimension of D-session data: {D_sessions.shape}\")\n", "print(f\"Data dimension of R-session data: {R_sessions.shape}\")\n", "print(f\"Data dimension of indices: {idx_sessions.shape}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Look at data \n", "Now we can look at the data structure.\n", "- D_sessions: 3D array of shape (n_timepoints, n_trials, n_features)\n", "- R_sessions: 3D array of shape (n_trials,)\n", "- idx_sessions: 2D array of shape (n_sessions, 2)\n", "\n", "```D_sessions``` represents the data collected from the subject, structured as a list with three elements: ```[250, 1500, 16]```. The first element indicates that the subject underwent measurement across ```250``` timepoints. The second element, ```1500```, corresponds to the total number of trials conducted. In this context, 10 distinct sessions were executed, each comprising 150 trials, lead up to a total of 1500 trials (150*10). Each individual trial involved the measurement of ```16``` channels within the EEG or MEG scanner.\n", "\n", "```R_sessions``` simulates the measured reaction time for each trial that the subject undergoes at different sessions. \n", "\n", "Lastly, we have ```idx_sessions = [10, 2]```. This indicates the number of sessions conducted, which in this case is ```10```. The values in each row represent the start and end indices of the trials.\n", "\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Prepare data for HMM \n", "When you're getting into training a Hidden Markov Model (HMM), the input data needs to follow a certain setup. The data shape should look like ((number of timepoints * number of trials), number of features). This means you've lined up all the trials from different sessions side by side in one long row. The second dimension is the number of features, which could be the number of parcels or channels. \n", "\n", "So, in our scenario, we've got this data matrix, ```D_session```, shaped like ```[250, 1500, 16]``` (timepoints, trials, channels). Now, when we bring all those trials together, it's like stacking them up to create a new design matrix, and it ends up with a shape of ```[375000, 16]``` (timepoints * trials, channels). \n", "Beside that we also need to update ```R_session``` and ```idx_sessions``` to sync up with the newly concatenated data. To make life easier, we've got the function ```get_concatenate_sessions```. It does the heavy lifting for us." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data dimension of the concatenated D-data: (375000, 16)\n", "Data dimension of the concatenated R-data: (375000,)\n", "Data dimension of the updated time stamp indices: (10, 2)\n" ] } ], "source": [ "D_con,R_con,idx_con=statistics.get_concatenate_sessions(D_sessions, R_sessions, idx_sessions)\n", "print(f\"Data dimension of the concatenated D-data: {D_con.shape}\")\n", "print(f\"Data dimension of the concatenated R-data: {R_con.shape}\")\n", "print(f\"Data dimension of the updated time stamp indices: {idx_con.shape}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a quick sanity check, let's verify whether the concatenation was performed correctly on ```D_sessions```. We've essentially stacked up every timepoint from each trial sequentially.\n", "\n", "To do this, we can compare a slice of our original design matrix, say ```D_sessions[:, 0, :]```, with the corresponding slice in the concatenated data, ```D_con[0:250, :]```.\\\n", "If the comparison ```D_sessions[:, 0, :] == D_con[0:250, :]``` holds true, we're essentially confirming that all timepoints in the first trial align perfectly with the first 250 values in our concatenated data. It's like double-checking to make sure everything lined up as expected." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array_equal(D_sessions[:, 0, :], D_con[0:250, :])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, it's evident that the concatenation process has been executed accurately.\n", "\n", "Next up, let's confirm if the values in ```idx_con``` have been appropriately updated. Each row in this matrix should represent the total count of timepoints and trials for each of the 10 sessions. In our case, it should total to 37500 for each session (calculated as 250 time points * 150 trials). " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0, 37500],\n", " [ 37500, 75000],\n", " [ 75000, 112500],\n", " [112500, 150000],\n", " [150000, 187500],\n", " [187500, 225000],\n", " [225000, 262500],\n", " [262500, 300000],\n", " [300000, 337500],\n", " [337500, 375000]], dtype=int32)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Look at idx_con\n", "idx_con" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Indeed, each session now aligns with ```37500``` datapoints. This means that when we pooled together the timepoints and trials, the count for each session ended up exactly as expected. It's a reassuring confirmation that our concatenation didn't miss a beat.\n", "\n", "**Please take note: If the measurements haven't been continuously recorded within a single session but have been pre-processed and exported on a trial-by-trial basis, we'll need to construct the indices in a different manner. In our case, where we have 250 timepoints for each trial, where each trial consists of 250 timepoints and there are a total of 1500 trials, the indices must be created by specifying the start and end timepoints for each trial.**\n", "\n", "You can create these indices using the ```get_timestamp_indices``` function. The following example will guide you through the process.\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Values in index:\n", "[[ 0 250]\n", " [ 250 500]\n", " [ 500 750]\n", " ...\n", " [374250 374500]\n", " [374500 374750]\n", " [374750 375000]]\n", "\n", "Shape of index: (1500, 2)\n" ] } ], "source": [ "idx_trials =statistics.get_indices_timestamp(D_sessions.shape[0], D_sessions.shape[1])\n", "print(f\"Values in index:\\n{idx_trials}\\n\")\n", "print(f\"Shape of index: {idx_trials.shape}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**NOTE: It is important to standardise your timeseries and, if necessary, apply other kinds of preprocessing before fitting the model.**\\\n", "This will be done separately for each session/subject as specified in the indices. The data provided here are already close to standardised (so the code below will not do much)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "D_con_preproc,_ = preproc.preprocess_data(D_con, \n", " idx_trials)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Load data or initialise and train HMM \n", "\n", "You can either load the `Gamma` values from a pretrained model or train your own model. If you prefer the former option, load the `Gamma_sessions` from the [data_statistical_testing](https://github.com/vidaurre/glhmm/tree/main/docs/notebooks/data_statistical_testing) folder. The GLHMM model in question has been trained utilizing a Gaussian observation model, incorporating mean and covariance parameters for 6 distinct states.\\\n", "However, if you would rather train your own model, you can use the variables ```D_con``` and ```idx_con``` as inputs and and complete this section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our modeling approach involves representing states as Gaussian distributions with mean and a full covariance matrix. This means that each state is characterized by a mean amplitude and a functional connectivity pattern. To specify this configuration, set ```covtype='full'``` and the number of states to ```K=6```. If you prefer not to model the mean, you can include ```model_mean='no'```. Optionally, you can check the hyperparameters to make sure that they correspond to how you want the model to be set up." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'K': 6, 'covtype': 'full', 'model_mean': 'state', 'model_beta': 'no', 'dirichlet_diag': 10, 'connectivity': None, 'Pstructure': array([[ True, True, True, True, True, True],\n", " [ True, True, True, True, True, True],\n", " [ True, True, True, True, True, True],\n", " [ True, True, True, True, True, True],\n", " [ True, True, True, True, True, True],\n", " [ True, True, True, True, True, True]]), 'Pistructure': array([ True, True, True, True, True, True])}\n" ] } ], "source": [ "# Create an instance of the glhmm class\n", "K = 6 # number of states\n", "hmm = glhmm.glhmm(model_beta='no', K=K, covtype='full')\n", "print(hmm.hyperparameters)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Train an HMM**\\\n", "Now, let's proceed to train the HMM using the loaded data (```D_con```) and time indices (```idx_con```).\n", "\n", "Since in this case, we are not modeling an interaction between two sets of timeseries but opting for a \"classic\" HMM, we set ```X=None```. For training, ```Y``` should represent the timeseries from which we aim to estimate states (```D_con```), and indices should encompass the beginning and end indices of each subject (```idx_con```).\n", "\n", "If you want to use the same Gamma values as used in this tutorial without training the HMM, you can just load ```Gamma``` using the code below:\n", "\n", "```python\n", "# Get the current directory\n", "PATH_PARENT = Path.cwd()\n", "# Path of the location of the data\n", "PATH_DATA = PATH_PARENT / \"data_statistical_testing\"\n", "# Load Gamma data\n", "Gamma = np.load(PATH_DATA/\"Gamma_sessions.npy\")\n", "print(f\"Data dimension of Gamma: {Gamma.shape}\")\n", "\n", "```\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Init repetition 1 free energy = 10424285.62136018\n", "Init repetition 2 free energy = 10424471.457711864\n", "Init repetition 3 free energy = 10418736.098348387\n", "Init repetition 4 free energy = 10416991.333350975\n", "Init repetition 5 free energy = 10413512.681879953\n", "Best repetition: 5\n", "Cycle 1 free energy = 10415552.22543761\n", "Cycle 2 free energy = 10404433.474428767\n", "Cycle 3, free energy = 10399962.798963035, relative change = 0.286776134646397\n", "Cycle 4, free energy = 10396844.666747313, relative change = 0.16667766582172433\n", "Cycle 5, free energy = 10394446.060602685, relative change = 0.11364481247008078\n", "Cycle 6, free energy = 10392542.188508492, relative change = 0.08274094040171934\n", "Cycle 7, free energy = 10390976.895951219, relative change = 0.06369365497784654\n", "Cycle 8, free energy = 10389665.812333891, relative change = 0.05064755831851465\n", "Cycle 9, free energy = 10388570.153590763, relative change = 0.04060691667219894\n", "Cycle 10, free energy = 10387666.949602965, relative change = 0.03238999653987988\n", "Cycle 11, free energy = 10386921.924499355, relative change = 0.02602225890732946\n", "Cycle 12, free energy = 10386287.517006794, relative change = 0.021678244089158263\n", "Cycle 13, free energy = 10385716.909915598, relative change = 0.01912522395732363\n", "Cycle 14, free energy = 10385170.102370033, relative change = 0.01799767397258085\n", "Cycle 15, free energy = 10384610.16658393, relative change = 0.018096267890629736\n", "Cycle 16, free energy = 10383997.916000562, relative change = 0.019403073440377155\n", "Cycle 17, free energy = 10383301.107497333, relative change = 0.021605716258255983\n", "Cycle 18, free energy = 10382516.406493783, relative change = 0.02375303620848365\n", "Cycle 19, free energy = 10381697.433626954, relative change = 0.02419075182650241\n", "Cycle 20, free energy = 10380950.45768706, relative change = 0.021587797053583206\n", "Cycle 21, free energy = 10380330.81922069, relative change = 0.017592666872900235\n", "Cycle 22, free energy = 10379827.818128241, relative change = 0.01408004023951785\n", "Cycle 23, free energy = 10379410.626232367, relative change = 0.011543260537671558\n", "Cycle 24, free energy = 10379052.20066883, relative change = 0.009819871789324648\n", "Cycle 25, free energy = 10378733.882826217, relative change = 0.008645632041986765\n", "Cycle 26, free energy = 10378445.424423078, relative change = 0.007773734066309854\n", "Cycle 27, free energy = 10378182.209982885, relative change = 0.007043466185127063\n", "Cycle 28, free energy = 10377942.33251607, relative change = 0.006378041737981543\n", "Cycle 29, free energy = 10377725.46424632, relative change = 0.005733196893441284\n", "Cycle 30, free energy = 10377531.738014368, relative change = 0.0050953116354012255\n", "Cycle 31, free energy = 10377360.167969914, relative change = 0.004492296457170692\n", "Cycle 32, free energy = 10377208.16697361, relative change = 0.003964134272520598\n", "Cycle 33, free energy = 10377071.903067501, relative change = 0.003541132134954935\n", "Cycle 34, free energy = 10376946.791559195, relative change = 0.0032407745681643577\n", "Cycle 35, free energy = 10376827.877472263, relative change = 0.003070783452279929\n", "Cycle 36, free energy = 10376710.105669716, relative change = 0.003032064244954175\n", "Cycle 37, free energy = 10376588.488315739, relative change = 0.0031212959269600053\n", "Cycle 38, free energy = 10376458.11672618, relative change = 0.00333481421767484\n", "Cycle 39, free energy = 10376314.160302727, relative change = 0.0036687951599837765\n", "Cycle 40, free energy = 10376152.449988794, relative change = 0.004104346080424937\n", "Cycle 41, free energy = 10375971.758992493, relative change = 0.004565155808635596\n", "Cycle 42, free energy = 10375773.913474215, relative change = 0.004973703219483623\n", "Cycle 43, free energy = 10375561.531036953, relative change = 0.005310796435162553\n", "Cycle 44, free energy = 10375337.870999215, relative change = 0.005561696584734983\n", "Cycle 45, free energy = 10375105.199401585, relative change = 0.005752501986735857\n", "Cycle 46, free energy = 10374863.256759092, relative change = 0.00594614831368073\n", "Cycle 47, free energy = 10374610.54661305, relative change = 0.00617244219819337\n", "Cycle 48, free energy = 10374345.637196878, relative change = 0.006428812175010359\n", "Cycle 49, free energy = 10374067.315469444, relative change = 0.006708987138883736\n", "Cycle 50, free energy = 10373776.412767604, relative change = 0.006963424126244926\n", "Cycle 51, free energy = 10373480.627518022, relative change = 0.0070305209264194205\n", "Cycle 52, free energy = 10373194.864750018, relative change = 0.006746472475274071\n", "Cycle 53, free energy = 10372935.29376709, relative change = 0.006090794732343945\n", "Cycle 54, free energy = 10372715.589104671, relative change = 0.005128896225937276\n", "Cycle 55, free energy = 10372541.971970867, relative change = 0.0040366452138654784\n", "Cycle 56, free energy = 10372412.027299708, relative change = 0.003012148222968975\n", "Cycle 57, free energy = 10372318.436656721, relative change = 0.0021647569094890616\n", "Cycle 58, free energy = 10372252.779479427, relative change = 0.0015163514414730298\n", "Cycle 59, free energy = 10372207.41145504, relative change = 0.0010466771043177278\n", "Cycle 60, free energy = 10372176.202932924, relative change = 0.0007194878717474443\n", "Cycle 61, free energy = 10372154.638114437, relative change = 0.000496912842780475\n", "Cycle 62, free energy = 10372139.568216968, relative change = 0.00034713142281389645\n", "Cycle 63, free energy = 10372128.866829285, relative change = 0.0002464431132436224\n", "Cycle 64, free energy = 10372121.120897517, relative change = 0.0001783498681604869\n", "Cycle 65, free energy = 10372115.396574657, relative change = 0.0001317850084604177\n", "Cycle 66, free energy = 10372111.075665914, relative change = 9.94658006658798e-05\n", "Cycle 67, free energy = 10372107.746128097, relative change = 7.663891636552954e-05\n", "Cycle 68, free energy = 10372105.130371124, relative change = 6.0205566554482105e-05\n", "Cycle 69, free energy = 10372103.038814038, relative change = 4.813800321820953e-05\n", "Cycle 70, free energy = 10372101.339909043, relative change = 3.909943316939458e-05\n", "Cycle 71, free energy = 10372099.940770814, relative change = 3.219941689365205e-05\n", "Cycle 72, free energy = 10372098.774599282, relative change = 2.683725940347362e-05\n", "Cycle 73, free energy = 10372097.792451281, relative change = 2.2601790737425705e-05\n", "Cycle 74, free energy = 10372096.957804253, relative change = 1.9207039174578052e-05\n", "Cycle 75, free energy = 10372096.242921904, relative change = 1.645072342656288e-05\n", "Reached early convergence\n", "Finished training in 496.58s : active states = 6\n" ] } ], "source": [ "Gamma,Xi,FE = hmm.train(X=None, \n", " Y=D_con_preproc.astype(np.float64), \n", " indices=idx_trials)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, the datapoints in Gamma correspond to the concatenated data (```375000```), and the number of columns represent the six different states." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(375000, 6)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Gamma.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data restructuring \n", "Now we have trained our HMM and got our ```Gamma``` values we need to restructure the data back to the original data structure. In this case we are not doing HMM-aggregated statistics, but we will instead perform the statistical testing per time point. \n", "We will acheive this by applying the function ```reconstruct_concatenated_to_3D```. It takes a concatenated 2D matrix and converts it into a 3D matrix. \n", "So, it will convert ```Gamma```, shaped like ```[375000, 6]``` back to the original format for number of time points and trials shaped like ```[250, 1500, 6]``` (timepoints, trials, channels)." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Reconstruct the Gamma matrix \n", "n_timepoints, n_trials, n_channels = D_sessions.shape[0],D_sessions.shape[1],Gamma.shape[1]\n", "Gamma_epoch = statistics.reconstruct_concatenated_to_3D(Gamma,\n", " n_timepoints=n_timepoints, \n", " n_entities=n_trials, \n", " n_features=n_channels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a sanity check we will see if ```gamma_epoch``` is actually structured correctly by comparing it with ```Gamma```.\n", "\n", "To do this, we can compare a slice of our 3D-matrix, like ```gamma_epoch[:, 0, :]```, with the corresponding slice in the concatenated 2D-data, ```Gamma[0:250, :]```.\\\n", "If the comparison ```gamma_epoch[:, 0, :] == Gamma[0:250, :]``` holds true, we're essentially confirming that all timepoints in the first trial align perfectly with the first 250 values in our concatenated data. \n", "\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array_equal(Gamma_epoch[:, 0, :], Gamma[0:250, :])" ] }, { "attachments": { "image.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Across-sessions within subject testing \n", "As we move on to the next part of this tutorial, let's dive into how we can use the ```test_across_sessions_within_subject``` function.\n", "This function helps us to find connections between HMM state occurrences (D) and behavioral variables or individual traits (R) using permutation testing.\n", "\n", "\n", "**Permutation testing**\\\n", "Permutation testing does not assume any particular data distribution and the procedure shuffles the data around to create a null distribution. This null distribution comes in handy for testing our hypotheses without making any assumptions about the data.\n", "This null distribution becomes our benchmark to test the question: is there any real difference or relationship between the variables we're interested in?\n", "\n", "![image.png](attachment:image.png)\n", "\n", " **Figure 5C** in [Vidaurre et al. 2023](https://arxiv.org/abs/2312.07151#:~:text=GLHMM%20is%20implemented%20as%20a,sets%20at%20reasonable%20computational%20time): A 9 x 4 matrix representing permutation testing across sessions. Each number corresponds to a trial within a session and permutations are performed between sessions, with each session containing the same number of trials.\n", "\n", "\n", "**Hypothesis**\n", "* Null Hypothesis (H0): No significant relationship exists between the independent variables and the dependent variable.\n", "* Alternative Hypothesis (H1):: There is a significant relationship between the independent variables and the dependent variable.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Across-sessions within subject testing - Multivariate\n", "The multivariate analysis aims to evaluate whether the state time courses of our brain data ($D$) contribute to explaining the observed variability in behavioral measurements ($R$) like reaction time. By quantifying explained variance, this analysis determines whether state time courses significantly influence changes in behavioral responses.\n", "\n", "If there is no difference in brain activity between sessions, it suggests that the brain’s response to the stimuli is consistent over time. However, if we detect differences, this could indicate that the subject's perception of the task changes over time, as reflected by variations in brain activity across sessions.\n", "\n", "To run the ```test_across_sessions_within_subject``` test, we set the following parameters:\n", "\n", "**Inputs**:\n", "\n", "* ```D_data```: The state time courses of the brain data (```Gamma_epoch```).\n", "* ```R_data```: The behavioral measurements (```R_trials```).\n", "* ```idx_data``` : Indices for the first and last trial for a given session (```idx_trials_session```)\n", "\n", "**Settings**:\n", "\n", "* ```method = \"multivariate\"```: Specifies that the test should perform multivariate analysis.\n", "* ```Nnull_samples```: Number of permutations (optional, default is 1000).\n", "* ```test_statistics_option = True```: Ensures the permutation distribution is exported.\n", "\n", "Additional settings allow for the inclusion of confounding variables, which can be regressed out during the permutation testing process. For details on these settings, refer to the function documentation.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total possible permutations: 3.62e+6\n", "Running number of permutations: 1000\n", "performing permutation testing per timepoint\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 250/250 [01:49<00:00, 2.28it/s]\n" ] } ], "source": [ "\n", "# Set the parameters for across sessions within subject testing\n", "method = \"multivariate\"\n", "Nnull_samples = 1000 # Number of permutations (default = 0)\n", "\n", "# Perform across-subject testing\n", "result_multivariate_session =statistics.test_across_sessions_within_subject(D_data=Gamma_epoch, \n", " R_data=R_sessions, \n", " idx_data=idx_sessions,\n", " method=method,\n", " Nnull_samples=Nnull_samples)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What we can see here is that ```result_multivariate_session``` is a dictionary containing the outcomes of a statistical analysis conducted using the specified ```method``` and ```test_type```. \n", "\n", "Let us break it down:\n", "* ```pval```: This array holds the p-values resulting from the permutation test. Each value corresponds to a behavioral variable and will have shape of 1 by q, see [GLHMM paper](https://direct.mit.edu/imag/article/doi/10.1162/imag_a_00460/127499/The-Gaussian-Linear-Hidden-Markov-model-a-Python).\n", "\n", "* ```base_statistics```: Stores the base statistics of the tests. In this case it is the explained variance $R^2$\n", "\n", "* ```test_statistic```: Will by default always return a list of the base (unpermuted) statistics when ```test_statistic_option=False```. This list can store the test statistics associated with the permutation test. It provides information about the permutation distribution that is used to calculate the p-values. The output will exported if we set ```test_statistic_option=True```\n", "\n", "* ```statistical_measures```: A dictionary that marks the units used as test_statistics\n", "\n", "* ```test_type```: Indicates the type of permutation test performed. In this case, it is ```across_sessions_within_subject```.\n", "\n", "* ```method```: Specifies the analytical method employed, which is ```'multivariate'```, which means that the analysis is carried out using regression-based permutation testing.\n", "\n", "* ```max_correction```: Boolean value that indicates whether Max correction has been applied when performing permutation testing\n", "\n", "* ```Nnull_samples```: Is the number of permutations that has been performed.\n", "\n", "* ```test_summary```: A dictionary summarizing the test results based on the applied method.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Visualization of results**\\\n", "Now that we have performed our test, we can then visualize the p-value array.\\\n", "We will use the function ```plot_heatmap``` from the ```graphics``` module.\n", "\n", "Note: Elements marked in red indicate a p-value below 0.05, signifying statistical significance." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# The alpha score we set for the p-values\n", "alpha = 0.05\n", "# Plot p-values\n", "graphics.plot_p_values_over_time(result_multivariate_session[\"pval\"], \n", " title_text =\"Multivariate - Uncorrected\",\n", " xlabel=\"Time points\", \n", " alpha=alpha, \n", " num_x_ticks=11)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Multiple Comparison**\\\n", "To take into account for type 1 error, we can apply the Benjamini/Hochberg correction." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pval_corrected, rejected_corrected =statistics.pval_correction(result_multivariate_session, \n", " method='fdr_bh')\n", "# Plot p-values\n", "graphics.plot_p_values_over_time(pval_corrected, \n", " title_text =\"Multivariate - Benjamini/Hochberg\", \n", " xlabel=\"HMM States\", \n", " num_x_ticks=11)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Cluster based permutation testing**\\\n", "In order to provide a more strict control over Type I mistakes, we can also apply cluster-based permutation testing to control the Family-Wise Error Rate when conducting multiple comparisons.\n", "To use this p-value correction, set ```test_statistics_option=True``` while performing permutation testing, as it is an input to the function (```pval_cluster_based_correction```)." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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tUCCvPIEWvO3SFecK1pUVjxs0aBD99ttv7rQLgEoJUaScRYwFAAAA4H38o44FskJVu8eCq21zQbmKiI6OptTUVHfaBUCVMRYyiLEAAAAAvI8/SKEMQaWTlvsLJFw6XK5RsWXLlgrXs7eiQYMG7rQLgEpBjAUAAADgW9gO9OlRCmU0BJExSMPJoL9z4A4uHe3tt99OS5YsEbUsuJCcTHFxMb355pu0bNkymjBhAgUCHE/Srl07kUYMVB8wLAAAAADfotiPPBZaToGES1KomTNn0u+//06PPPIIvfDCC6ISN3PkyBFRP2LAgAGiaF4gIOcj5mD22NhYbzcnIKtvI8YCAAAA8D5IN1sexFg4AFfb/vnnn2nx4sWiKN7x48fFcq5yfcstt4jK1soCdAB4MsYCHgsAAADABwvk6VAKFWQMEpOW+wskXC6Qx4bD5MmTxQSAN7NCmZBuFgAAAPA6fhG8bSidtNxfIKFJ5e2ioiL6448/6OLFi0IW1blzZy12C0CFhEEKBQAAAPgUkEKVJ9CkUA77Z9auXUt33nknpaSkqJYfPnyYOnToIOIqOKi7a9eudOutt5LZbPZEewEoH2MBKRQAAADgcwXy9OixCDIYNJ8CCYcNi4ULF9L//vc/SkxMVC3/xz/+QceOHRNxFZwlatiwYfTdd9/R22+/7Yn2AiBAjAUAAADgu1IoLo6nx9F6g9FAQRpOBh16barFsNi1axcNHjxYtWzv3r1iYuNi0aJFIjvSTz/9RP369aMvvvjCE+0FoJzHAgXyAAAAAN+SQulRBsWg8nY1GRaXLl2iFi1aqJatWbNGnLA77rhDtfymm24SqWcB8BSIsQAAAAB8VwqlTLKiJwxBBs0nV2qkNWnShMLDw6lXr160Y8eOCrc9cOCAyMjK23Of/I033iBdGBbR0dGUl5enWsa1LDg7FB+0kri4OFEsD4BqyQpllkiSkBkKAAAA8B2PhT7TrBqDDJpPzsBFph999FGaPXs27dmzRyREGjp0KCUnJ9vdnvvmzZo1o5deeonq1q1L3sbhq962bVtasWKFdT49PZ02b95Mffr0EUaHkrNnz/rEwYHAiLFgipByFgAAAPCdGAsdBm77ghRq/vz5NGXKFFHOoV27dvTBBx9QZGSkiHW2R48ePejVV1+l8ePHizpzukk3+9hjj9Ho0aNp+PDhwphYuXKlsJLuv//+ctuyROqqq67Suq0A2I2xkOMswmyMDQAAAABUH6wgkAnRrWHBciht9+dM+Ybdu3fTzJkzrctYGcQxztu2bSM94LBhMWrUKHrllVfo+eefF6lnIyIi6Nlnn6Vx48apttu+fbuY/u///s8T7QWgXIyFNc4iymvNAQAAAAKeYkWMhV6lUJ6qY5GVlaVazt4FWw8Dl3TgUII6deqolvM8l3fQA05d9RkzZlBqaqoohJeTk0Nz5swptw1rwa5cuUKTJk0ib6LnwBfgvMcCKWcBAAAA35FC6bGGBRNkDNJ8Yho2bEixsbHWad68eeSPOF1522g0lrOklLAngydvIge+sC6NjQo2FDjwhTNV1a5du8LAl7Fjx9L06dO90mbgbowFDAsAAADAm5hM+jcshBRKw6YbDGXxxzExMdbl9uIhuFYc97MvX76sWs7zeold1qefys8DX0DVhNqJsQAAAACA9zAX618KZTSUaD4xbFQoJ3v9zdDQUOrWrRutX7/euqykpETM9+7dm/zSY+Hr+EPgC6iaUJv82KhlAQAAAHiXYrP+PRZhhjwKN2jXPS4x5FkHsdkbwcWkeaoIVtxwOEH37t2pZ8+eQnWTm5srBsuZiRMnUv369a1SKu73Hjx40Pr/8+fP0759+0TGVtv6c9WB3xkW1RX4UlhYKCYZ26Ac4FkghQIAAAB8NyuUXg2LECqkECrQdH/Mzp07VVKoiuCkSByrPGvWLFGcukuXLiLbqtyvPXPmjBgwl7lw4YIqE+trr70mpv79+9PGjRupuvE7w6K6YEtx7ty53m5GwFI+eBt1LAAAAACfkULp1LAIJpOYtNyfszzwwANisoetscCJh3ypSLA+BXCVUF2BLyy1yszMtE4clAO8F2MBKRQAAADgXfyh8nawwaT5JEuhOO6Xs5b6M37nsVAGvtx0002qwJeKrD9XsJd/GFQfCN4GAAAAfNew0GuBvAhDDkVq2HTJkOOUFCogDQvWdlVVPITrRzRo0ICuu+46evzxx6l58+ZUXVRn4AtbnjxxXAeoPhBjAQAAAPhuHQujTg0LX5BCBZxhwQElK1asEIXlhg8fbu18Hz16VASYdOzYkQYOHEjHjh2jRYsW0ZIlS2jz5s2ieF51UJ2BL3J0Pwdvc8ETUD2gQB4AAADgW5j9oPJ2iKGIQgyFmu4vkHDJsKhXr57IvsRZlriwnBI2JgYMGCB0ZFwbgo0Nzr379NNP008//UTVhZ4DX0DVIMYCAAAA8N2sUHqVQoVRLoVruD8z5TqVbjYgDQs2GPik2BoVDHsveB3LjFh61LJlS7rvvvv8NlgFUijfqGOhrPYJAAAAgOrHH+pYKAOutdofgxiLSjh37hwFB1f8UV6nzJLEHgFlzQd/AlIo7wApFAAAAOC76WaNOpVChVIRhWqY2yiUAksK5dJVb9++Pb3//vvlUroyHNPA63gbmRMnTmia6hWAoCCDajQEhgUAAADgQ1IoG2WBXggiExk1nIIQvF01HNgsB21zSlc5eJvjK77//nsymUy0cOFCsaygoIA++eQTsb0/AimUd+MszObS844YCwAAAMCX6ljo07CIFOlmtevTFRvyKZBwyWPBwdlbt24VqWSXL18uKlDz9O2334plvI63kdPOctalBQsWkD/CMihOVcvaOeC9AG7UsQAAAAC8S7E/VN72gQJ57777rggj4D50r169aMeOHZVu//XXX1ObNm3E9pyZddWqVar1d9xxhygToZyGDRtGnsBlERmnZ/3hhx9E8bnk5GSxrHbt2qo0rgBUVy0LSKEAAAAA35FCBQfrsz9opCIKJqOm+3MmeHvZsmWiHtsHH3wgjAquxTZ06FA6cuSI6GfbwoP5t99+u0iaNHLkSPryyy+FmmjPnj3UoUMH63ZsSHAJCBlPFXl2+6qzIcFByzzBqADViVK/WYSsUAAAAIDX4LT9/iCFijDkiurb2k25Tn3//PnzacqUKSKzKns42MCIjIy0hhjY8uabbwqjgYtRt23blp577jnq2rUrvfPOO6rt2JDgeGd5io+PJ0/gsiXAReb4oLnoHFeo5on/f+edd9Lp06cpUGB3FV94dnEB70mhEGMBAAAAeA+FCkrfUihL5W0tJ0cpKiqi3bt30+DBg63LeNCe57dt22b3M7xcuT3DHg7b7bmGG3s8WrduTVOnTqXU1FTyGcOCC+OxNfTZZ5+Jvw8//LCYunXrRp9++il1795duGw8hS9pzxBj4RspZyGFAgAAALyH2eY9HKJXKZTpbzKaDmk4/S32y2UJlJO9MgxcfJqTAfFAvRKe56yr9uDlVW3PfVrun69fv55efvll2rRpk0iq5InEQy7FWDz11FPCgtq7d6/oqCv566+/aNCgQWKb7777jrRG79ozoB1hihgLBG8DAAAA3sNcrJYkG3XqsaDsb4i07G/nlf5p2LChavHs2bNpzpw5VB2MHz/e+n/ut3fq1ImaN28uvBjcZ9cSl8xJtnQeeuihckYFw531Bx54QDTWE+hdewY85LGAFAoAAADwGsr4Cj3HWJDZAxORKBydmZlpnWbOnFnuqxMTE8loNJarE8fzFdWD4+XObM80a9ZMfBeXidAalwwLrlMRERFR4Xru6PM2WuNL2jN2Ydm6tYAXYyzgsQAAAAC8htnsH1IoKvHARCQyQikne8qY0NBQEVbAkiVrc0pKxHzv3r3tNpeXK7dn1q1bV+H2zLlz50Q/NykpibQmyNVUs//3f/8nLC5buIPNNSvYK6A1vqQ9Y2mVnA2LJ1sXF6jurFAwLAAAAABfkULpNXibJA9MTsBy/48//pgWL15Mhw4dEoPdubm5QqnDTJw4UeXt4BjnNWvW0Ouvvy5ioFletWvXLqEeYnJycoRqZ/v27XTq1CnR1x09erQobs0D7T4RY8HF8LgzzgHRfKCtWrUSyznOgU8EW0GOFADxFVzRnvFF5YuvNKhgXHivjoUJ6WYBAAAAn5FCGfUuhdJyf04wbtw4unLlCs2aNUsMgnfp0kUYDvIgOWdlVZZ36NOnj4gffuaZZ+jpp5+mli1b0vfff2+NI2Zp1f79+0X/PCMjg+rVq0dDhgwRoQGeiCd2ybAYOHCgyKzEFtBLL72kWscngLNFcQVurfGG9qwiw4IvBgK8vUsYYiwAAAAAn8Dkb1IoLffnJOxtkD0OttiLYR47dqyY7MGhC2vXrqXqwuWrznELnBXqwoULIl6BJ/4/Z1vSOsLcn7RnQNvg7XBjPl2d9Ae1iN7l7eYAAAAAAQukUJVLobjeGScd0pOip9o8FkrkLEq28Qes8bKXo9ddWH40adIkUSujZ8+eIt2srfasfv36og2y9qx///5CezZixAhaunSp0J599NFHVu0ZS7tuueUWcRzHjx+nJ554wmPaM6Ado2rMoIdvXUOhRhPtSr6aiO73dpMAAACAgKTYX7JCSRp7LKTSP1zvjIO2/R23DQt7sBfBbNZSoOY/2jOgHVJQqDAqmA4J+4iKC4mMuGYAAACAt6VQwQq5sq7wcoyF3vGIYeFpfEl7xi4tnjxRvRBUziXDtdSWvhX/Dw8uIErZSlRH+9geAAAAAARIHQsfiLHQMzo1J32HadOm0cGDB4WLC1QvjXuMUi+4tM5bTQEAAAACGsRYaJNuVu/AsAC6pVmrpkTxV1VuWEgBNlQAAAAAeAG/KZDnocrbgYLDUqi0tDSHd5qXl+dqewBwjrrXE6XvLf1/2m6iwlSisJpERVlEBz8m2vMaUYubiTo9QhRXWm8FAAAAANriN3UsIIWqHsOC6zoYDI7dJJIkObwtAG5RdwjRoVcsMxLR5fVEjW4jyj5F9MeM0sWH3ifKTyMa8iWRQacjKAAAAIAP43dSKC33F0A4bFhwFiYYC8DnqNWXyBhOVFxQOn/x51LDYtfr6u3SDhIR7l8AAADAE5hMfiKFgseiegwLrksBgM/BRkWt/kSX1pbFWfz9FdGhT4nCFbZEsxFEMIwBAAAAj1DsLx6LYo3jIorLCuRxiQNO+sOTv6LLdLMAlIuzuLC29EGQf4bo59JiiSpkjwYAAAAAHOfcWaJgiciYQxSVSBRZ27/TzXpICrUzQArkOeSnWrJkiYibcBb+DH/Wn+EaFlyinS1R4EXDgo2KdCLK4qebJXmA8pYtSPVW6wAAAAB9Ys4n+qwZ0ZLGRJ+3J3puuOMF8vQuhdJyCiAcuuqPPPIItWrVil555RU6efJkldsfO3aMXnzxRWrRogVNnz6d/BnUsfAB4joShcZXvk2h41nNAAAAAMDWQYS6Zxxe4P/B20g363kp1IkTJ+iNN96g119/nWbOnElNmjShrl27UtOmTSk+Pl54JtLT04XRsWvXLjp79izVrFmTHnroIb83LIAPwLETScOILtt4x+CxAAAAANwjUyqNWWQiiyrcDFKoSvYXQDhkWERFRdG///1vevLJJ2nlypW0YsUK2rp1Ky1fvtwqkeKMUc2bN6f+/fvT6NGjadSoURQSEuLp9gNQSu/3iVreXzZvLiD68zWi85agbngsAAAAAOfIzydKl4jqWOaDcx0ukKdbj0VxWcC1ZvsLIJwK3g4ODqabb75ZTExxcbG1cF5CQoKIdgfAK4TFEtXvp1527keFYQGPBQAAAOAUaalEGYp5g3KmYikUF8fTbYkCpJv1XlYoNiRq1arlXgsA8BThNcv+X5hOJJW4VyCPvXN6fVACAAAAzpKeSpSpmJfyiQoyiMLjKpVC6VYGJRsCWnoZSiigQLpZ4L+EJZT9n42KokyisCqCvG0NiffvIDq2jigqjyimJ9HjP3ukqQAAAIBPeiyUhgWTfboCw6KsBx0SonPDAh4Ll9FpLjAAnPRYMAVOxlmwd+LicqLoi0SGTKKzv2vaPAAAAEB3hkXWKbubqj0WOu5eIt2sW+j4ygPghMfC1TiLvKiy/8cUEZUEWBQWAACAwJZC2YZVZJ2uOsZCr4HbyuBtLacAAoYF8F/c9VgwyYqhhqBiokv73G8XAAAAoBePBZeuKKzasDApPBYhejYsPOSx6NGjhyiozIWV/RnEWAD/xV2PRUkJ0cl0oqaKZSc3EdXrpknzAAAAAJ8mNaX0L8uhalcuhSpWxFj4hRRKy/0RiULKMTEx5O9oduW5nsWvv/5Kq1evpuzsbAoU2PJkC5QtUeDjHgtna1mkXCLKMhPlK5ad3qxJ0wAAAABdSKGYDOekULqtYaEskKflFEC4ZFhwsbzrrrtOZVQMGTKErr/+ehoxYgR17NiRjh8/ToHAtGnT6ODBg8ISBT5GSA2ioGDXq29ftDw8LQM2gtO/lXoyAAAAgECQQjGZNlmh7GAy+YlhYfbAFEC4ZFh8++231LNnT+v8N998Q+vXr6fnn3+efvzxR1E4b86cOVq2EwDn4axOSjmUsx6Li2fKGxb5aUTJf2nTPgAAAEBvhkV+CpGpfAVuc7GfSKEkjeMrJAooXLry58+fpxYtWljnly9fLuRAM2fOpBtuuIGmTp1KGzdu1LKdALiG0rBw1mNxwTIqY/uxU5vcbxcAAACgFylUuZSz5b0WxWY/8VhAClX9hkVwcDAVFhZaZVDsrRg2bJh1fZ06dSglRTnMC4AvVN920mNxyeKxyCGiIsVDEnEWAAAAAil424GUsyZ/MSwghap+w6JDhw70+eefU3p6Oi1atIhSU1NFbIXM6dOnKTEx0b2WAeArHgsmP6Y05R4/XH9fUVqVGwAAAPBXzGaiTItF4UCRPJUUSs+GBepYVH+62VmzZtGoUaOsxkPfvn1Vwdw//fQTsiQB3yBMA48FUxxOlCY/WU1Eh9YQtRuuTRsBAAAAXyMjvez/IqQipPT9V4HHwu8qb2u5vwDCJcOCsz/t2bOH1q1bR3FxcTRu3DjrOvZiXHvttTR69Ggt2wmAa4QnuJ8ViknsRnRuVdn81gUwLAAAAPh/4LZMSCKR6WKFmaHM/lIgT2svQzEFFC4XyONgbZ5siY+Pp//+97/utgsA7T0WRRlEJcVEQcaqP5edWTrJNBlAdGkNkVwA6NwhDzQWAAAA8FHDIqJ+mWFhVwpVZlgY9WxYFGrsZTBRQOGWr2r79u00b948mj59Oh09elQsy8vLE96MnByOeAXAhzwWTKHCtetIqlmZ+s2Iuo8vm79whCjHSQ8IAAAAoLeMUDIZwUSHiWgvEa3eVW5zs79U3kaMhVu4dOWLiopozJgxIraCi+W99dZbdPbs2dIdBgWJYnlvvvmmey0DQGuPhTNxFsr4CiapEVHvSWXz7Pn4c7UGDQQAAAB8OCOUTGEQ0RVLpsRsM1F+VoVZoXQthUJWqOo3LJ599llRCO/999+nI0eOiJSzMuHh4TR27FhasWKFey0DQOusUM7EWSgzQjFJjYla9ScKr1G27H8rNWggAAAAoAMpVOMO6vnjv/tnHQsYFtVvWCxZskQUwbvnnnsoIcGm40ZEbdu2pRMnTrjXMgC0rmPhqsciLJwooRZRSBhR+6Fly/9aTWQu0qihAAAAgI9KoaKiiFr0Ua9PLlWq2Es3a9SzFIpjIoo0nEwUULh05ZOTk6ljx44VrjcajSLWwlO8++671KRJE+Ed6dWrF+3YsaPS7b/++mtq06aN2J7bvWqVIruPpXI4y7dq1qxJBoOB9u3b57G2Ay97LApd8FjUbURksIy+dLmxVC/JruBz2UTfzdewsQAAAIA25J48Sn/OnUGn//MQXZl1N+WlprrusYivSdRpjHp9unqgrjAn3/r/oAIdx9nCY1H9hkXDhg3p8GGO4LHPli1bqEWLFuQJli1bRo8++ijNnj1bBIl37tyZhg4dKowde2zdupVuv/12uuuuu2jv3r100003iemvv/6ybpObm0v9+vWjl19+2SNtBj7ksShwwWPB8RUyHYYTHSMiXs233JrPNGooAAAAoB0Fyz+mjmtfp8Zr3qZavyygy79vcN1jkVCTKKIGUUK9smUX/lZtbsrjKrKl5G75jXSLDxgW7+p4AN0lw2LChAn04Ycf0rZt26zLuKHMxx9/TF999RVNnDiRPMH8+fNpypQpNHnyZJHu9oMPPqDIyEhauHCh3e05iHzYsGH0+OOPC4nWc889R127dqV33nnHus2//vUvUfRv8ODBHmkz8CLBkURBoc57LC5WYFjUSCRKiCubP3MUVbgBAAD4HLnGSNV8NJV1/F3yWDD1WquzI1qQSkqoWCrrUobGRJNuKfLAFEAD6C4ZFpwJqk+fPqIQHlfcZqOCU842atSI7r33XtGR53mt4WxUu3fvVhkAnIWK55VGjhJebmsw8AWqaHvgZ7DBq/RaOOCxyE9NoZJL56zzV7I5qbWC1l3L/p9rIkop2xYAAADwBbJIMahGRJGF2a5nhaqZWPq3vsKwOF9mWJiSk6k4qKw0Wnh8DOmWYo29FcWBNYDukmERGhpKa9asoUWLFlGzZs2E+6WwsJA6depEn3zyCa1cuVLEWWhNSkoKFRcXU506dVTLef7SpUt2P8PLndneUfh4s7KyVBPQQZyFAx6L/L8PUJAioUVemM3Iy7VlleYF58sXCgIAAAC8SWaROjNTWH6Ge1IoW49FThpRVqnxUXjmDM349R8049cJNH3DRBrVN4p0i4ekUFk2fUbuR/rjALrLlbfZS/HPf/5TTIEIFwacO3eut5sBHIE9Fpysgvv/4VV7F0xHD6rmgxo1V2/Qa4R6/tBOoi7XaNJUAAAAQAuys/Moz0wUaenpGVMvOP5hlvjak0IpPRay1yImkQpOn6a4gjKpTp02Cgmx3mBDQEuFc3FZfLISljrNmTPH4QH0imKbPTWAXu2GhTdITEwUnpDLly+rlvN83bp17X6GlzuzvaPMnDlTaOBk2Pq0vWmAD1BcTLTqItEWfsoSUf+jRIo6d/bIKTbQ4RSiuBCi+BCi0Obt1BvUqk+UWI8o5UKZYQEAAAD4EJwFKqOwzLAwXFanh62U7GwupV25x0KOs2jbV3gslIQ10rFhwTERRu0Ni7Nnz1JMTJlELCwsjPwRlwyLgQMHOuTRWL9+PWkJS7C6desm9suBKUxJSYmYf+CBB+x+pnfv3mL9I488Yl22bt06sdwd+Ibw15vCr2BJ3pH0UqOC2Z1CZDIRhYRU+JH0oAj68VKZC/nfbTuV36htD6LfLEUgYVgAAADwMfLS0ym9kKierEq6ZFP41VEZlNKwqNWYKDi0rIaTJc6i8HTZvoPj4ym4hqKYrN4o1thjUVL6h40KpWHh6wPo1RpjwZ15rratnMxmMx0/fpw2btxI586dE9t4AvYScOapxYsX06FDh0ShPo525yAXhrNRsTdB5uGHHxbxIK+//rpwI7HbadeuXSpDJC0tTaTeOniwVALD1cR53hE3EqcE4+CaHj16eOR4gQZc16Xs/zklRJvXVrp5bkqKOuCtpk3KWtmwkDl/nCjLwTS2AAAAQDV5LNKVGYkuq70KlSGlXFEvSEgsG6xLalEuM5TSYxHWuDHpGg/FWPTo0UP0F7nf6MgAuow8gF7RgLg8gK5EiwH0avVYsPFQET/++KOoyM1R7Z5g3LhxdOXKFRHdzh3/Ll26CMNB1pedOXNGBLrIcPaqL7/8kp555hl6+umnqWXLlvT9999Thw5lpel/+OEHq2HCjB8/vkL9my3Tpk0TE0uhYmNjPXDEwG1unUr06QYuC1o6/81iokEjHTIsQiIjKTRSnbKvnGHBHNxJdLWiKjcAAADgRfLS0oQUygoPgOXlEEVWnQr25Lq11Ewxn5KTQxbTolQOdfagqpYFx1j4hQyKYWNMy8LhJaV/du7cWaXHQh5AnzRpEnXv3p169uxJb7zxRrkB9Pr164tYX3kAvX///mIAfcSIEbR06VIxgP7RRx+pBtC5f3zhwgXrADrDXg2tPRuax1iMHDlSBHSz9GjTpk3kCdjbUJH0yZ7RM3bsWDFVxB133CEm4Ke0G0M0dAzRT1+Xzq//odTNKwejVWJYRCVaH6Vq2nZXz7McCoYFAAAAX/JY2CYeYq9FU5u4QTsUX1YrNoISa5fNKAO4Lx0jKjarPBbh/uCx8IBhodcBdJ8I3m7evLkqf64/wy4tnjiKH/gwt0wqMyyKiohWLiWaOM3uprmpqVUbFjEJRA1aEJ3jMtyIswAAAOB7Hot021gBjrNwwLAouaLW7Ec0aVo2owzgNpuo+OQBMqel+Y/HgrtzWqr5pcAaQNfSJhNwrAVX3uYAlECAZVAcm8EuLuDDXDuUpPhaJOUTSTlE0isvk3TKfv2JPEc8FkzbHiSZiaQsImnNRpLmziFp+vTSfWdmeuIoAAAAgCoxFxVRYXa2WgrFfdzjh+jsiBGUMe42Mk27v8LPJ8fWpBVFBlpvMtAfxUEUnlSvbGX91iQVEklniaTdRAW//KT6rO5jLEo8MJFjMRb+gEseizvvvNPu8oyMDNq+fbtw3XgqxgIAlwgOJrrmBqJ3FpfOp50lWr2SaOqDrkmh5DiLb5cQCalpFtFfiromffoS9eun6SEAAAAAjpCfni7+5piIzCVEwZZhZNOiJdRgwy7rdhebNaekxx4r9/lzEdG0zlSaczW6Zk3qpZDeSClFRKvLti1YrU6IonuPRT6nNtXeY7HTwRiLgDQsfv31V5FOVgnPx8fHU79+/ejuu++mIUOGUCAAKZSOGDuxzLBgfllVtWFhLyNURQHcAAAAgI/EVzASGSijSKLE8NLlOenpFK/YLraCwN3c9DJpU1R8gnplr75EIQYiU2mPufB/6qKyuo+xKPaMYREouGRYnKpAQhKIICuUjujZVz1vKF8Bp6S4WOhSZSIr81i0uoooyBB4Tw0AAAA+jfI9xgHcsmGRevmKyrCI4JhDe59Pz7D+PzI+TrXOEBxMUrMEoiOlxkvh+bLvMoSGUkhtRaC3DpFKiCQNDQspwLoImsdYAOCrGLigYURE2YLm7cttk5+RQZKiBkulUqiIKKJXlxJ9s5RodGnBRkFCAlHHjto1HAAAAHACZRISOc6CX23Jl7KoRNnRtdTvKvf5jFIpFRMVpzRFLEycav1vIWutFDIog0I2pVclVL6k4USlIMZCwebNm13a+bXXXuvS5wDwGDySIufbVjx4KyqOV6lhwcbK0NvEX2nvX0Qrvi9dmJ3NJTY1azIAAADgDPlKj4XFKZGVXWpccEdXLsZNBw/Y/XxumsKw4MEyW/55D9G/nxf/LfSn+ApLGYsijffHIMZCwYABA8rFVFQGV+Lm7QMh7gAxFjqDYyZkwyLNfcNCtV8Zk4koJ4eoRg332goAAABo5LGQ1U25SsPiwIEqPRaRcWopFGNo2JCkdu2Ex6PQn+IrLCEWZo33F0g4ZFhs2LDB8y3RKYix0BlKA0ADj4UV2+143zAsAAAAeNljkW3gAIsCq2GRp9zwzBmSsrPJYPO+yrNklarQY8EMGUrSgYN+57Ewa2xYmCmwcMiw4FLhAPidYWFjRNjWsHDZYyEbFk2auNZGAAAAQCOPRWFkPBUWXaTcPDuGBXPoEFHPntbZovx8MhUUWOej7HgsBMOGUeF//+tfNSws58eo8f7kGAuj0WgdkPZXPFJ5GwCfpabCUEhNpaLUVPrhjklUIz+fIjIyKa1PH9XmkZWlm63KsAAAAAC8WMeCMcXWooyMi9b5coYFy6EUhkWu4rNMZLyd4G3mmmuoMDSUSJFZyl88FiaN98cgxqIKCgoK6Ntvv6U9e/ZQZmYmlSgy6TAcY7FgwQIt2giAdigNgLQ0urJmDdX5saxq6HlFLFF4TAwF80PT2f1W4A0BAAAAqttjEV6zFqUnh1nDrE1hIURBRu7I2Y2zUNawYKJt61hYMEREUGGbNkT79/uVYVGscVxEMQUWLhkWp0+fpuuuu07Us4iLixOGRUJCgqi8zUHMiYmJFB0dTYEAgrd1htIAMJupKMoawiaQsrMcq2FR2X4ZeCwAAAB4CVU9pvh4Sk8tU/rHNUwgqpFEtG+f3cxQyhoW8ucrorBBA7VhkVX2DtUruRrXx8ulwMKlZMOPP/64MCa2b99Of//9t8gCtWzZMsrJyaGXX36ZIiIiaO1adYl3f4V1cgcPHhQuLqADbAyAnKwsVeCZKTPL+fgKhgP3jQpVJgwLAAAAXq68zURFRVFc0yQKtryi4pvXJWqvqONUzmOhlkJF2RTIU9GqlXWEOiQ0lIL8oBqcLIXSajJTYOGSYfHrr7/S/fffTz179qQgSyEUNi7CwsKE0TFo0CB65JFHtG4rAJobFpknT1C6YmiikGtQuGBYiHTMVWScAgAAAKrbYxFRrx61/W0HXZ2cTJ23bKHEz38haqcwLM6eJUnhabCVQkVVIIViGs6fT1evXUu9k5Opy99/k6F7d/IXKZSWE4MCeZWQl5dHTSwZbzgQhTtV7MGQ6d27N82YMUO7VgKgFTbGQsbp05RmMFBdyyhLQX6+ax4Lhg2L5OQKa2QAAAAAnkZkdcrLUychSUwS8p6YPrXEMknpsZArcF99dblUs1VJocSg2pAhojPpL9mAOBQ9ROP9MQjeroRGjRrRuXPnSncQHEz169cXsqgxY8aIZSwNCg/nvMkA+LbHIuPiBWFYEJUaFkWchMASwB3laEYoe/tG8DYAAIAKSD9+nBbfMobCDEEUKpVQ12eeoatuHevy/i4eOUxf3ncfRV+4QBIXaVUQaa8ORfv29JdUmiGK5cB1P/6YWlkMi9yMshgLTmASGhFBgUSBq3KeSvYXSLhkWAwcOJBWrFhBs2fPFvN33HEHzZs3j9LT00V2qM8++4wmTpyodVsB0N6wSE62GBZEnNfMpMgK5ZLHQgZSKAAAABWQcfoU1fhfWdDzya1b3DIs9s6ZS3EbN5V1ZBXvMrtp05s2pQMGA+VavPWGHTuolWVVrkJGxcXxhFcigECBPC8YFk899ZRw6RQWFoq4iqeffpouXLhA33zzjSj+MWHCBJo/fz4FAsgKpTM4W1lICJFlRCcjNY0yDBajwmZT5w0LdY0MAAAAwB7KpCFMWIl7Qc/hHNuwdKn4vzr5v32PhSEoiMLCwynXIv8tumKR8bIUKqNMChUZV7EMyl+Rg6613F8g4bIUiicZlj393//9n5gCDbmCYlZWFsVyZiDg0/DIi8SjN5cuifn0rEwqNhgoy06lTafSzTLwWAAAAHCAQpvsScE2tcCcpaR+Pco3EEVI5esmVFToNZT1/hbDojC4LKogV5FuttKMUH5Kvo/vz9dxSUa2atUqjNAD/WJ5yPJjPNMS4JYWZLAGWGkihcrJIUlRjRQAAACQyS0sVHkWjCb3BDM5WZl01pJPtpxhYS/Ggr0kPXpY/19Uu7bdrFCVZYTydymUllMg4ZJhMXLkSKpTpw7dc889tH79+nJVtwHwaSwGAyeWLbGMGnGcRaGWhgUDrwUAAAA75GRkUJEidMEgV8F2kez0dOcNizp1rP8vVMRVKD0WkQHosdCyhoVJIYVCutlKWL16tSiIxzEVCxYsEJW2b731Vho/fjxdc8012rcSAC2xGADK2qJpBqJErQ0LzgyVlORiIwEAAPgr2WxYkIHCLRkJZUmSO/tLNgZRgYGoWKGyCqtRg4wcV2iHMEUa2UJFilmVxyJAYyy0TJ1rCrB0sy55LIYOHUoLFy6ky5cvi+xQQ4YMoS+++IIGDBhADRo0EMXxtm3bpn1rAdCCBIthoch0wR4LlXDJYKCISnJ328XWELHjsUg7d45Sz56llOPHKRcpaQEAwC8ozM+nIpY3lZRQgaLQakXkZKSrPBbFOTlufT/vTzIY6FywUeWxqOw9FqbwZJiysqjEbBbFjpV1LDgrVKDBvqN8DacCCizcMspCQkKELIqnoqIiqyeDg7jfeecdMpsDTVkG9Oqx4B+/iWuvFJYKosJCQ8kYHKypFIpfNu8PGkRpR48Ki7721b1oxpatAZfKDwAA/I23hg+n879tpmCJyBweTq9lZlJwBZ4CWbpUpKihpJQiuQLvj2E5VEtTmWkRXMn7JdTG6CjKyCApLIxKFDG0kXGBKYUyary/QEIzb09OTg4lJycLL0ZBQYGwegHwacNC8cANCg4miV2+lmxRoW7styLDIiw6mjJPnbK6CdOOHoNRAQAAfgAPHIXI3Z6iokqNCqsUSvH8V0qRXIH3x1w2BlEzxX5LcnMr/IxSCiW3wRSqfvtFOeu59wPMGhsDZgos3DIsMjMzafny5cJLsWHDBjKZTNSxY0f6z3/+Q+PGjaNAAHUsdIhFssT1K2Rik5LILJUtCCkqIslsJoMzXgtbl7GNYVGUn0+FJcVWo8VQWCgMcBgXAACgb0yKx3iwJFX5bM9OTyOzYrVWHosSg4HMilpN5rQ0MuXmUkhUVKVSKLkNBeFhqmWBmBWq0Mf355eGBVfW/uqrr2jdunVCAtWmTRtRJI+NCf5/IIE6FjrE4llQjg/FJSVR9wEDqfbKlVQcG0sJbduWvhic2C0bIRLfA5mZdg2LU7t2kUmSrIaFKSeHMs6cofjGjd09IgAAAF6ksLhsXNogSVSQmUkRlciI2MNgVBgeRenpbg00ZSuK2tXo0I5iUtMpsmlTajV6tF2jwq4UKj2dclkSrCAQ61hwnlMtc52WUGDhkmExadIkatasGT322GPCmOjUqZP2LQOgGqVQcUn1qPfzz4vJbW+I1bBQB2cf3baVzApNLXNqyxYYFgAA4Ad1KSIU8znJyZUaFpxuNkIpWTKZyJyXV6ERUBW8P5laN46iu2f/p8rP2JNC5dp4LAKx8ja/oTkQXrP9SYEVGuBSVihOmXXs2DF64YUXYFQA3RoWlu6/1WOh5b7teSyObWXDQmlWEJ3cskWb7wUAAOA1svPUsQxsWFRGlgjeVi9zVQ5VVFAgslLJxDgoX7InhVJmhArUrFCSByYGdSwqoVu3bqr5lJQU6tmzp0g527t3b63aBoBnqFlTBFNlKUYk4uvX12zf9gwLTkF4bPs2kcaWjQs5yI89FgAAAPQLj0hnZGZSbQcNC96e08NG2YyKsxSJGjZ0SwbFRDuYySmUpbvcBsuIuvBYFCj9LoEZvF3MtUA0DH0s5n8k1LFwCg5cPnXqFOW7WeAFgGohPp4ybR7onvZYXDh0kPIsrmqT4rsv/fkn5cvSKQAAALojLzub8s3qPEKVGRYFeXlkNpnUtZPcyAwlZ4SSqeGgYWEICio1LpQxForieKGRkRRskyUqEPCUxyJQ0MSwAEBPGIxGyoipoVrGMRZaFt+zNSxYBiVTGmdRNnJ1Zvt2bb4bAABAtZNx5YoYlZYcNCzkDE7KdLPuSKHk/cnUcCKTk1IOZSuFCkQZFMNpW7SeAgkYFiAgyYiKVs3H1dPIY6Gsvp2WRpIlDTEHbsuE2mTZgBwKAAD0S0bKFavM1SHDIsO+YSGkUC5gK4Vy1GNhG8DNHpMcpWERgMXxmBKLFEqrqSSw7AptCuRFR0fT7NmzRaYoAPRAhk1Kvfh6HoixYN0qu6hr1qSjW8oMixZ9+5Hp2HG6cOCAeODsXL6chv6n6gwejsAekI1fLRMvNWNJMXUeMozqt23r8GfXfrqYTFlZFGQupl633kI1GzSk1YsWUHFuHgWZzdRn3HiKr6eRd6cSTEVFtOLdtyk8OIQMZjMNffAhClbUFNn581q6cPiQeIC16N6DWvfp6/E2AQD0xe/fLae0c2fJWCJRx+uuo0adOtt97m34ainlJl8pfWYOHU71nUybn34l2eqNDrHEK+RcuUKnjxymE3/9RUlNmlB0VDTlp6VS8okTZLR4AsoFb1dhWBzevYuSz52jpMaNKTI8nLIvJ9OVkycoN9jossciVOGVYMPm0qUL1vmImMBMoa+1fEmiwMIpw4Iraq9YsYJOnjxJNWvWpJEjR1JSUhJFRUUJwwIAvZCheBCHGAwUqdHITF5EBJ23vCwKiOjKu29Tflg4pZw6ad2mcdeu9Pfly5QRUtqGrKNHRPyFFm3IvHSJPpv4L+v8+qVL6PVtOxz67Nn9/6Ov7plind+98VeaMPPf9M3UqdZl+7dvo5nLviZPs+q/82nVrGet84XBRhrz4MPWjsAHN98kChgKEhNp4fmLHm8TAEA/FOTk0MLxZYV6V7duRe/sP1Buu4wLF+jziROt878uW0avbd3utBRKjp+LsHQjeXBn0zdf09J5L1BoiUQJxWXVDLredZf4W8xeDkVHrCop1E8LPqb1ny6miJISii0u6652mDRJG49FWhpdvHjeOn/yyGEKRGBYVJNhkZycTH369BFGhZyTNzIykr7//nsaPHgwBSqovK1PMhS/9DijUbPq1+ezMuklZbXuF14ot02NxERq3KOHGH2SObh2DXUfN97t769Ru7Z4iMlHE250fOwgQhkfwi+c4GAKjVVnsAh3phK5G0g236M8Dr5WEleWtRgWYUb1aB0AAIRHR1NJUBAFlZRU+iysUaeO6pnJzz1nkQ0LWynUlb17qK6pfN9g86KFRCHBVq9FsOSYx+LcXwfs7u/3zz8jsgyWGYODKdyJWhi2UihTfr5VIx8ZgBmhyFLDQtM6FlT2byDgcIzFc889JzI/TZ8+nX788Ud64403KCIigu69914KZLjq9sGDB0UaMaAfMuTRbjYsLC8eTXDAdZzUpg21HXy9atnJPxzzKlRFevJlMimehwaTOlNJZWSkpZamxVN8NjsrW1011In9aVXFVm6LDA9sqKrcVlObAAD6ytRkUhQmq+g5wc9MpUHgyvMkIyWlXGKO3CtXKPXiJbvblygrbttU366M1IsX7O/PRgblzECZMng7nzNCKd6NMbVqUSBS7IEpkHDYNP/5559p4sSJ9Nprr1mX1alThyZMmEBHjhyh1q1be6qNAGjOvZ8sprT/7RMGRrAyLsJdHKiHwen7Gve7SrUsppHzucvtceHkSfFyC5XzkmdlOf1Zo+WzRZmZdPEULyMKtbyfi5zYnztkZGWpRhHzFRKBzNQUKiguJrk+bFF2tqgTEhSEXBQAgFL42cWDLGFVPLsunuTtymIjijKdf8YppVAyeamplBIVYX1OKVF2NM1BRqISc5VSqGKzmdKSL6tqZdgzVJyRQTGhCq9EZn4BXYwMoyCSKEgiGj5yFAUiEpf20DDgWqLAwmHD4syZM/Tkk0+qlvXr10+MHl6+fBmGBdAVNa65RkyNNd5vg5496alfN1g1vldOlsVW1G7ajMKio6hBh44UGRsrUvnlWl4kHNCnBRdOnrCMmpU+yrIvXRa/UUdGsEo/W/Yizrp82bo/2VDJvmR/BE5rLp4+JbTHIZZ55Xm8cKLU2JEpMZko8+JF7YocAgB0j+2zMCclRdSOCGYZZSXbZV++5PAzU5UVykYKVSxJlJx6hUIssXQta9el7POl8Qvh8fGUlV1qwJSEhVq9BJVJoS6fPUsFJSV0ybK/ZjFxwnhhImomUK6lloWjxfHsSaFyggxkDuKDKD2QdtdcS4EIYiyqybAoLCykcJtMOvK8WeE6AyCQYYOhVd9+Dm1bu1kzOmkxLK5oZFjw6Jvy5VaUl0vZKSkOubRLR/jKXrDpZ8/S+RPHVctSTp1y+qXrChctL3t5FDH5xHFVO5WSA9nwgGEBALD1wMpw6u+0M2eodvPm6u1OnBCDGDKFOTmUk5oqYuGcj7FQS5xYolRo8aTWbdvWalhwgTwrERFEuXlVSqHYAGLdf6HlO+q0akUnt20rbXNhoXW7GCfjIpRSqGxhVJRRr1lTCkQ4qJ4nzfZHgYVT2gGOsdizZ4912r9/v1h+9OhR1XJ5AgBUTK2mZemZlSPy7iDc+jbFeK4oOuVVfVb5YmTX+9kjR9SGSn6+yDzlSdhwuXjqlKotKo+FdYSxDKXhAQAAsoxTib3nrO2zpnQ75wZ60u1IoQptO+mty1LYFpnKam4bIiPLPlOJFIqPp6L9FRZwDsJSouPiXZZC5SjkpBwAHpcYmDEWnqq83aNHD2rXrp1I+OPPOJX+4NlnnxWTLffff79qXh7RRKYkACqmlqLuS8rpU1RSXExBbmY4st/pPkHNe13t4GerfulyJz4uSaOCgnbISkul3KwsMiozrKSkUH5WFkXExJSORFoe1vImWknJAAD+Qalksvyzqz0NLvfcM5Xb7gQ169HToe/hfk5Wqhy8TRUaFs26dqVNdoKtg2vUsP6/KCODpJISMtiJF+PnnkxoeDg16tiR5NKqZk5AYjmGGu5IoRTnoV7TZh73TAeaFGrnzp0UE6POtBjQhsWiRYs82xIAAgyWQskUm0yUdvYsJTZp4tY+Sw0BdafbEW8Ia4+Tz55VvUj45ZeVnkbBtqN5J044LPdyBZYmMOVf9sepcZerSkfuLPnfZbV0ipMjjAAA/4YNhmLLc0zuptuTnJZKpmyemU4MVGSnp4nkEfakUDIR0dHUpEsX63yJwqscojAE2KgwZWdTaGysXXmoTFKTplS7WamkSyqXFcp1KRTHWMjUaxqYMiiynE8Nc0WSlvvyK8Nikk0BFgCAe8gvBuUomTuGBadXFEGE7C1U/LgdkUJdPnPG+nKUX8Ty6Ft1ewfkkbly8oQTJ4RhwR0GeX1ZDAYMCwBA2UDJ5bNnSgcgFFntbJ8TOZmZwkNq+8x0Rlopx1dYn52hoVRSVKTyWHAnXfm8LzHY9xjIcih7hoX83CvbX+nAlDg0VVYo16RQUjnDomzgK9DgWBZlpi2390eBBfIzAuAlatmMCDmr663sxaPslDvS6bZ+1vIiVu3D4h3Qqp1VIWuJy+mjT5ykwvx8Sr140aVjBAAEBsnnzglpKaOO1TpRYdxCRTFdjsZXCLjytqWjLgdZyx6GsMhIiq1bt5yHIdwm3bm9zFBy3Jmy08+DUOxhth0NdzYrVEh0NBmMRiowlAYtl7XZPe+5nvFUjEWgAMMCAC8RV78+BYeFadY5VmpwlUXynDIsFC9Y5YtW9dL1uMfiRFn1U2PZI4pHEdUdgfIxGAAAUNkgC3fSy7az/zxx5hmn9FgwUbVqiY5koZ3Rf46rs/UwRNWpo/q8vcxQXLuHPdJl+2tKIeHh4h2i9H4wNRKc81iwccJyqGybuI5A9ljwOdV6CiRgWADgJbigW60mTTXrsFc0+sY1HgqV6Q3tfrZsNEyy5HlXVaMN184Aqgrlyz44Olr1vRcUx2gbg+FpTwoAQB8o4xFUAdU5OZStMASUBojyeZJ+/rzIgOeKYRGblFQas6EKhC59zrN8yTalTXTduqp5e5mhOBBdCXtA5P0p4zVckULJcixl4DYFumFBBs2nQAKGBQA+khnK3ZSpypdkWIxao5tik6qwss9GW2peKF+08jKGX8z5itEzrVG2hUf/lIbXRcUL1l72KwAAUA5OUKi69rWqJo7ymRmnfmZeqeKZaVscjwkJDaXYevVUMigmydJJ5zgL29HrmHr1qpRCKZ+JTD3Le4PfH+U8Fk5KoeQ4C2V8BWcnrN2wIQUqcuVtLadAAoYFAD4SZ3HFxk3vzss0vpH6pZB8/ITDn01o2Ej8NStGWRIalS7ztHdAGUPB1LS0hUk9e4bOHT9WcUegimMEAAQGSu9t+WfXSbueWvm5Z93OxkvgiGERV6sWRdeuXb6GhcXDwM9729HrWJvvteexUB4PS5fqNGps2V95D4izWaEYlkIpDYs6DRuWq1AeiFmhtJwCCZ8zLLhwSJMmTURV7169etGOHTsq3f7rr7+mNm3aiO07duxIq1atUq3njtqsWbMoKSmJIiIiaPDgwaKgn5IXXniB+vTpQ5GRkRTngrUPgBYpZzlGILeSAkmOFLiTqdeqtcOGQGlgYNln67ZuLXTASglBUmub/XnIO3DxdNmL3vZ7ORXjmcOHrPP1mjenGkqPBqRQAACbEf6kli1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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pval_cluster =statistics.pval_cluster_based_correction(result_multivariate_session)\n", "# Plot p-values\n", "graphics.plot_p_values_over_time(pval_cluster, \n", " title_text =\"Multivariate - Cluster correction\",\n", " xlabel=\"Time points\", \n", " num_x_ticks=11)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Conclusion - Multivariate test\n", "In the permutation testing across sessions, we examined the relationship between state time courses ($D$) and reaction time ($R$) across experimental sessions while preserving trial order. . Using synthetic data, the test identified significant differences from 27 to 230 timepoints. Although this extensive significance is likely due to synthetic data limitations, it highlights the toolbox's effectiveness in detecting across-session differences. In real experiments, such periods could reflect critical cognitive or neural processes depending on the task design." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Across-sessions within subject testing - Univariate test\n", "The univariate analysis examines how each state in the brain's activity ($D$, represented by gamma_epoch) relates to the behavioral measurements ($R$). This method looks for patterns between individual states and the variability in behavioral responses, such as 'reaction time', across sessions.\n", "\n", "To run the ```test_across_sessions_within_subject``` test, we set the following parameters:\n", "\n", "**Inputs**:\n", "\n", "* ```D_data```: The state time courses of the brain data (```gamma_epoch```).\n", "* ```R_data```: The behavioral measurements (```R_sessions```).\n", "* ```idx_data``` : Indices for the first and last trial for a given session (```idx_sessions```)\n", "\n", "**Settings**:\n", "\n", "* ```method = \"univariate\"```: Specifies that the test should perform univariate analysis.\n", "* ```Nnull_samples```: Number of permutations (optional, default is 1000).\n", "\n", "Additional settings allow for the inclusion of confounding variables, which can be regressed out during the permutation testing process. For details on these settings, refer to the function documentation.\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total possible permutations: 3.62e+6\n", "Running number of permutations: 1000\n", "performing permutation testing per timepoint\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 250/250 [07:49<00:00, 1.88s/it]\n" ] } ], "source": [ "# Set the parameters for between-subject testing\n", "method = \"univariate\"\n", "Nnull_samples = 1000 # Number of permutations (default = 1000)\n", "# Perform across-subject testing\n", "result_univariate =statistics.test_across_sessions_within_subject(D_data=Gamma_epoch, \n", " R_data=R_sessions, \n", " idx_data=idx_sessions,\n", " method=method,\n", " Nnull_samples=Nnull_samples)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Visualization of results**\\\n", "Now that we have performed our test, we can then visualize the p-value matrix.\\\n", "We will use the function ```plot_heatmap``` from the ```graphics``` module.\n", "\n", "Note: Elements marked in red indicate a p-value below 0.05, signifying statistical significance." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot p-values\n", "# P-values between reaction time and each state as function of time\n", "graphics.plot_p_value_matrix(result_univariate[\"pval\"].T, \n", " title_text =\"Univariate - Uncorrected\",\n", " figsize=(8, 5), \n", " xlabel=\"Time points\", \n", " ylabel=\"HMM States\", \n", " annot=False, \n", " num_x_ticks=11)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Multiple Comparison**\\\n", "To be sure there is no type 1 error, we can apply the Benjamini/Hochberg to control the False Discovery Rate" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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6wKIPw/rLrT60aCd6RXnwLI7laVEorQSt79F6ItqykW8fNLDQYOOLL74wHfHpL6i6Li2yob8i68Pg7bffXmw9MedHi2k5f6nWXAl9+NQWoXR/teUcp3Bss1Yo107rtIM0LcqjrSFpp28a1Op6ozWw0PoV2uFfcVci1kBci6vpedUHZw0A9Zz269dPhg4daucA+Wjv2doak7a6pa2uaaMF+gOCtuqmD+TFUUxLG2HQdet9oIGKLluD02CdKT7xxBMmYNXXZ86caRoo0Mr6+h4Ngm+44YYCr1dzZjTQ1XtQgyQNLrQlL532dbSXX2V0ALErTtucDfdGAABCow/CvXr1kv/+979FKsYWLbQ4jwZi2ijBAw88EO7NiUmaq6NBhxbLyt18LoDYRj4mALiAFndx9gXhVppboYqzfgWC27RpU540bcL6hx9+MBXnCSoA5EaOBQBEsZ9++kkmTpxoiqBph31acfx41i1B7NBiVFpkU3OItHiidg6oxem04QkNLo53cUMA0YccCwCIYtoCl9bh0d6yp0+fTlCBYqP1frR1K60TpPVHtPK2drCpwSxBBYBgyLEAAAAAIsDs2bPl2WefNf3IaHFEbcK8d+/eEi3IsQAAAAAiQGZmprRt21bS0tIkGtHcLAAAABABevXqZYZoRWABAAAAV9JGLbQfqXDyeDym49TcfRvp4DZRH1gMfWObGZ9Q3d/sXfq0P8z4jPNOstNmfvq7PV2ugtUb7tZNVqdbqko1qyOjhFL+0mF9r/L3mPrS61aze+f1buVf5me/B7xXrVlhbc9T//J3HPTtKn+nSpn7ssy4dj3/sqdPXmytO8F/0ZWr4L/YNq/fbcZlyvpPV06OVTWmem3/et45eLIZn735ezstqYz/PR1Pb2TGTzo6lbr8y4Vm7Lzgt2zcY0+PvN9Kf2JcOTvtrAusnlsPHTxip3056Tczzj6SbadVqeY/J7t3Hsjzum8fkiuXtdP2eOdzci7n8CFrnZl7reOoHhrc0Iz/3OWvtHrXFP8yxds57VPld9hJ/9lTxZqY6ViRs/XKBd5xZUeatYsiUx1p1qoD/eWYPtU7bu9IeyXXa7nNC/K67/3O/sB8n5P7HWmlHdNbrFH6WH9Sd99pvsQx369BtuEs/6Tz/XmW45DuCfK6cx82Bzk++bz/aLr3y3+78n1PsO05J++5TZ8S5D2Oc5y++ijLPsY+dO/imG9ukNcbHX09BXWsc1NcAtZzr3fs/3gU8bZUmv6fYlrfYMc/jg7cneesuPnOSX7nw3ctyrggx8JxjkcMsD7D1eyvlplx/ROr2mlZBw+b8ZiKzf1vOjnX8gpyX3qPUfql1neKmjK3oj09Mtv6LhrV6qCdNvCrMtZ7ilDqYvXMHHt67MjvzLjXlW3stH+X8n+Aphe8b748pjy9xp7+eZ41/e2UMwt9nQdcs77PQMchF+8jw53r/szzvan+sc77fJBx9PUEO5bd3/BPX73oFzPeuGannVbT+0xwcL91LagKFf3PAeddZt1c2/7eZ6f9udT6kP9rxVY7rVTpBDMuW956zlGZe/znu3Si9Uywz5Hme05o1cHfVPXGtbv87/d+3+7Z5f9+Pvuilmb8S8ZaO+3DTu3M+D+Zvg97kdvaptvTL3xvdRq6dtV2O23SG9aX239G+d9z8MDhPM8/a1Zuy3OsPN5nCOdz3TvPWdtlL+vgQUmuVEcOZ/m//8OhQoUKsm+f/9wp7fhSO650m6gPLAAAAIDcNKdCg4qO53wgCaX8PwKXpOwjmTJ/5tWybt26gN7q3ZhboQgsAAAA4FqlSleQUqXDE1jEeUuEaFDhDCzcisACAAAArqXP9rmqOJToumMJgQUAAAAQAfbt2ycrV660/1+9erXp9b5KlSpSv359iXQEFgAAAHCtuPg4M4Rr3YUxf/586d69u/3/4MFWqwx9+vSRceOcLUVEJgILAAAA4DhKTU2VhIQEGTBggBnyc9ZZZ5nmaaMVgQUAAABcSytQ5+5HoiTXrTIyMqi8DQAAAESzSAgsYoW/NzgAAAAAKCJyLAAAAOBaNDdbcggsAAAA4FrR1CpUtKMoFAAAAICQEVgAAADA9ZW3wzX4mptt2bKlpKWliZtRFAoAAACuFQl1LDJipLlZciwAAAAAhIwcCwAAALhWfHycGcK17lhCYAEAAADXooO8kkNRKAAAAAAhI8cCAAAArhUJlbdjBTkWAAAAAEJGYAEAAAD3ivP3vl3Sg3hzLOjHAgAAAIhykVB5O4N+LAAAAACgYMixAAAAgGtFQo5FrCCwAAAAgGvRQV7JoSgUAAAAgJCRYwEAAADXoh+LGM2xmD17tlx88cVSu3ZtUybtk08+CfcmAQAAwAV1LMI1xJKICiwyMzOlbdu2rm/jFwAAAHCbiCoK1atXLzMAAAAAxcHurC5M6/Z1kJeQkCADBgwwg1tFVGABAAAAuE1GjHSQR2ABAAAA16LydsmJqsAiKyvLDE5HDmdJqdJJYdsmAAAARC46yIvRytvHMnz4cElJSQkYvv/ixXBvFgAAABDzoiqwGDJkiOzevTtgOL3XoHBvFgAAACK88na4hlgSUUWh9u3bJytXrrT/X716tSxatEiqVKki9evXl6SkJDM4lSp9KAxbCgAAgGhAHYsYDSzmz58v3bt3t/8fPHiwGffp00fGjRsXxi0DAAAAEDWBxVlnnSUejyfcmwEAAACXoPJ2jAYWAAAAQHGKj4szQ7jWHUuiqvI2AAAAEG1SU1OlZcuWkpaWJm5GjgUAAADcXRQqPrxFoTLoeRsAAACIbrQKVXIoCgUAAAAgZORYAAAAwLVoFarkkGMBAAAAIGTkWAAAAMC1yLEoOQQWAAAAcK94kbhwldGJl5gSY7sLAAAA4HggxwIAAACuRc/bJYfAAgAAAK5FHYuSQ1EoAAAAACEjxwIAAACuRc/bJYccCwAAALhWXHxcWAeVmpoqLVu2lLS0NHEzciwAAACA4ygjI0OSk5PF7QgsAAAA4FpU3i45BBYAAABwLQKLkkMdCwAAAAAhI8cCAAAArhUXb1XgDte6Y0mM7S4AAACA44EcCwAAALgW/ViUHAILAAAAuBaVt0sORaEAAAAAhIwcCwAAALhWfFycxIep8nZ8jOVYEFgAAADAtahjUXIoCgUAAABEiLS0NGnYsKGUKVNGOnfuLD/99FO+806aNEk6duwolSpVkvLly0u7du3knXfekXAhsAAAAIDrK2+HayiMiRMnyuDBg2Xo0KGycOFCadu2rfTs2VO2bNkSdP4qVarIf/7zH5k7d678+uuv0q9fPzNMnz5dwiHO4/F4JIrdcO8SM960bpedFp9gxUs52Tl2Wk6Ofzd9J/mzf/1op5Wf+i8zfuWMLDtt6a+b7Ok1K7eZsfNond6jqRnv33/ITtuxNdOM9+w8YKeNO62FPf1izn4zXvH733aab9u3b9lnp9088DR7evXyrXn2YcnCDWb8SfMO/g363ju+0Z/00Cb/PvS8u5YZ//KxtQ3q66nW8TtyONtOK1U6wZ7OybGOYUrlco59tLbzvN6t7LT211Q048bfv+/f13mn29O//2xtb/VayXZa/cZVzfjwYf95+nu9/zz6jlHj5tXznLta9SvZad9NX2bGyZXL2mkntbL2VS1bbB2D8hWT7LTGzWuY8dPL/POJ/7CIjJO8PvCO/Zso8pl3XMWR1s0xPdk7dn4ezPOO/yPBWYdKZIcj7Vfv+El/0guJ1nlIrbHCTjuj7yn+Gf6yRulFuMO7N3L8U9MaPXGLdQ+oQ4es62VYpnUc1daur9vT1cbfmnehFbzbM8KxHsfnbbDt7D7AGr96of8euyD5YzOuP/c6/4wP5F1OsGV3f8Gx8Jfz7qNc5Uir433vNXm3N79j2v1Z78QrUjznwfl9dKp3fJEjbY9/0nlcc5/H9NWFX3fQ7bjTkei/nfJe7+os77iyI8265S0rvWP/7S2y1Bo9cbH/WvtzqXXzVHN8djRscoI9/c9qI814bteHCrYPzuuhi2MG77Umaxxpvo/x34Lci8c4rgH30GX+yec77zXjfyVZn5nGWO/ypkjh96FR3mvNyXnddf/QO3G1Y4Z7veM/836GpQ92vLdLkM+wYf6kF5pan0eDN3tvdDUo7/WbPtexzH7eiY7+tDHNre+DrZus46SW/rrRnh6b6P0+fS/3njruY3WOf3LBebPN+PWva+dZZvYR//fPDXd0NeM7hiXaac7tDXYsfK87z82rn1ufV/PS/Qf1L+8zhGrSwvrczNznf974e8NuM66QXMZO27PrQJ5rflx1//NE/93WZ7/H8Wywc7v1DHLupSfneYZQt3WwNvjZmf7vCl/dg9qO79VTGvqP/4fTrC/HZo7v1Wkf/pLneaHpydZ+JSb5S9lvWLMzz3p6XdHGTvvcu5zJp7W30/65fHnQc+/TumM9Mx59cmM77Ynd1vF99Bbnh4zInj17JCUlRfo//KsklnHccyXo0MG98sawNrJ7925JTvZ/juVHcyhSU1Nl1KhR9nNYvXr15K677pKHHjr655xP+/bt5cILL5Qnn3Q8NJQQciwAAACAMDt06JAsWLBAevToYafFx8eb/zVH4lg0r2DmzJmybNkyOfPMMyUcqLwNAAAA14qEytt79jiymDXTNynJDE7btm2T7OxsqVHDXxpA6f9Ll3qzdIPQ3JA6depIVlaWJCQkyCuvvCLnnnuuhAM5FgAAAMBxVK9ePVMsyzcMHz682JZdsWJFWbRokWRkZMhTTz1l6mjMmjVLwoEcCwAAALhWJPS8vW7duoA6FrlzK9QJJ5xgchz+/ttfD1fp/zVrOisRBdLiUk2aNDHT2irUH3/8YQKXs87yVXYrOeRYAAAAwLXi4q0O8sIxxHkrrWtQ4RyCBRaJiYnSoUMHU0/CRytv6/9dujhbUDg6fY8WiwoHciwAAACACDB48GDp06eP6ZuiU6dOMnLkSMnMzDRNyKqbb77Z1KfwFaXSsc7buHFjE0xMmzbN9GMxevTosGw/gQUAAABcKxKKQhXUNddcI1u3bpVHH31UNm/ebIo2ffnll3aF7rVr15qiTz4adNx5552yfv16KVu2rDRv3lzeffdds5xwILAAAACAa0VCq1CFMXDgQDMEk7tS9rBhw8wQKahjAQAAACBk5FgAAADAteIclajDse5YQmABAAAA14qmOhbRjqJQAAAAAEJGjgUAAABcK9oqb0czAgsAAAC4lq+zunCtO5ZQFAoAAABAyMixAAAAgHtRFqrEkGMBAAAAIGTkWAAAAMC14iSMzc1KbOVYEFgAAADAtSgJVXIoCgUAAAAgZORYAAAAwLVobrbkEFgAAADAtbR+RdjqWMTFVmBBUSgAAAAAISPHAgAAAK5F5e2SQ2ABAAAA16IoVMmhKBQAAABwHKWmpkrLli0lLS1N3CyiciyGDx8ukyZNkqVLl0rZsmWla9euMmLECDnppJPCvWkAAACIQnHxcWYI17pVRkaGJCcni9tFVI7Ft99+KwMGDJB58+bJjBkz5PDhw3LeeedJZmZmuDcNAAAAQLTkWHz55ZcB/48bN06qV68uCxYskDPPPDNs2wUAAIDoROXtGA0sctu9e7cZV6lSJdybAgAAgCikFajjqbwde0WhnHJycuSee+6R0047TVq1ahXuzQEAAAAQjTkWWtdi8eLF8v3339tpWVlZZnDKPnJIEkolhmELAQAAEB1FocKVYyExJSJzLAYOHCifffaZpKenS926dQNajUpJSQkYlmS8EdZtBQAAQOS3ChWuIZZEVGDh8XhMUDF58mT55ptvpFGjRgGvDxkyxNS7cA4np/YP2/YCAAAAiMCiUFr8afz48TJlyhSpWLGibN682aRrzoT2a5GUlGQGJ4pBAQAAID+0ChWjgcXo0aPN+KyzzgpIHzt2rPTt2zdMWwUAAIBopfUrwlfHIk5iSalIKwoFAAAAIPpEVGABAAAAFCdyLEoOgQUAAABcSzvHiw9T60zxMRZYRFSrUAAAAACiEzkWAAAAcC1ahSo55FgAAAAACBk5FgAAAHAvsixKDIEFAAAAXEsrboet8nZ8bAUWFIUCAAAAEDJyLAAAAOBalIQqOQQWAAAAcC06yCs5FIUCAAAAEDJyLAAAAOBacfFxZgjXumMJgQUAAABci6JQJYeiUAAAAABCRo4FAAAAXItWoUoOgQUAAABcS+s5hKujurgYq2NBUSgAAAAAISPHAgAAAK5F5e2SQ44FAAAAgJCRYwEAAADXovJ2ySGwAAAAgGvRQV7JoSgUAAAAgJCRYwEAAADXovJ2ySGwAAAAgGtRx6LkUBQKAAAAiBBpaWnSsGFDKVOmjHTu3Fl++umnfOddsmSJXHHFFWZ+zR0ZOXKkhBOBBQAAAFwrPs7qeTssQ1zhsiwmTpwogwcPlqFDh8rChQulbdu20rNnT9myZUvQ+ffv3y8nnniiPP3001KzZk0JNwILAAAAuL6ORbiGwnjhhRekf//+0q9fP2nZsqW8+uqrUq5cORkzZkzQ+VNTU+XZZ5+Va6+9VpKSkiTcCCwAAACA42jPnj0BQ1ZWVp55Dh06JAsWLJAePXrYafHx8eb/uXPnSjSI+srbI6+aY8Y3jmqd57XsI/7plMpl7enqtVPMuNeIjnZaUllrOee2OWin7d6x3572RZwej8dO+2rKEjPu3O1EO23r5r1mXLV6BTvtgo9/tKerVl9txls27rHTWnWoa8a161ey02Z++rs9/bd33vIVEu00x2bYzi1j7cOj1f37+v1by+zpulOsfatW0f9m3zorppSx055vUM+/0D+sUbeFs+2kZq1qmfGXk36z0z697DszThnjX/euHda+qkuuO8WMp7y30E57q35LM17UbqadNmqzf92Nm1fPs48JpaxYeNd2/7n58CFrPbW+6WenfXfqz/b0GTs6m/Hyi6bYaQuPeM+Z/zSJ+A+/n/8SkUe3W9mQi35ca6elNLH297RzmtppG9bstKcHP/CRGVdeP8BO+6j6djO+cmNVO+3BbRvt6WZnWVmZs77wHnwR2ZGSacZ7Pzhgp517qfWh1KD2Uv9G3uWf/FfOOjPevH63nVazrnXtn9TKn126fYu1bFW9drI10cP/m0P3Z6zxv+uMttOOSGkz3pQz1k6rtcl//B8++W8zHla7hn+DtlqjA7PW2Eljsv3n23dOf5tvbbd62zs+MM2/jwsrWyeldQf/sSg3w/8rzS1zrG2/fL3/WjvxJOta+qp0gp1W9j/WPqgbT5plxpM29LTT9uy0ln/45Wz/sahoXfvS179b3Z0/Rvk2aag/aXS1Q2Y8/3v//eDku8679KnsX2Yj74T/kMras98348VHutppq3b7j+/AOOseTnd+NvwVZBsb5n09wOPWqP+hFXbSa7usHZvQo41/vsvyvjVg3cF319a9i3eiiSPxJmv06Dsn+NN+DbKtjn1IX12Adah/+Sc/XPKnNXa8/MqqxtbyHnW8Py7IfjmXH+SHyMWzvzTjRxZbn3lqyybre0Ft8F7LXxywPq9VzROt+7LzJ/7vku1b95nx7h3+6/z1Df57OevgYTP+6Uxru9WDv1nLkQ8c23iDY+PmB9mJ/0mea1rWW6ON3/jXffXHy+3pzdV2mXG9rf7PsE4trPsu/W7Huv0fe7LqWuuzsFSOf3sf+b22Ga9evs1O86ywvvvevPozO61M2kP+BTXPe97t++U2xz6U909WjLM+k2vUbu5/T3frvrv2O+9nnnrdu+xjPb9tdqz7Ye9bT7fOh9nHeOtYXH+h/xFr+Ev+i6XDadYFfMdM/3e6+D6S/V9tIs0c07Pznqc3kqzvnWEVd9hp/7fE+yb/x7pctsT/WTilbi8zrtvQ/1D7n8wqZrypiePzfI3/w+cC72PE3ZX8N2F8QlyeZ6tDB62Hrm7drWca9fFa//dh9pEcMx73kvW8oJq0sD7Dzpz0rZ3WqJn/4S0xqVTAZ7ja+rd1Pw1r4N/ved96P69u8V+TkdYqVL16jmcr/ZoYOlQee+yxgLRt27ZJdna21Kjh+O7Ua7dGDVm61PFdH8GiPrAAAAAAItm6deskOdkfyEZCsaXjgcACAAAArhUJPW8nJycHBBbBnHDCCZKQkCB//23l+vvo/5FQMbsgqGMBAAAA1/djEa6hoBITE6VDhw4yc6a/iHhOTo75v0sXZ9nOyEWOBQAAABABBg8eLH369JGOHTtKp06dTL8UmZmZppUodfPNN0udOnVk+PDhdoXv33//3Z7esGGDLFq0SCpUqCBNmjgrsZUMAgsAAAC4ViRU3i6oa665RrZu3SqPPvqobN68Wdq1aydffvmlXaF77dq1pqUon40bN8opp/gbi3juuefM0K1bN5k1y2qYpCQRWAAAAMC1SstBSRRHK1wl6Ij4WxstqIEDB5ohmNzBgva47WyxNNyoYwEAAAAcR6mpqabDu7S0NHEzciwAAADgWglxh80QrnWrjIyMY7YK5QYEFgAAAHCtUnLYDOFadyyhKBQAAACAkJFjAQAAANdKlEOSKFlhWfcROSSxhMACAAAArlUq7rAZwrXuWEJRKAAAAOA4SqVVKAAAACC6JcghKRWmIkkJ3vXGSqtQ5FgAAAAACBk5FgAAAHCt0nGHpHRcVtjWHUsILAAAAOBa9GNRcigKBQAAACCycixWrVolWVlZ0qJFi+JcLAAAAFAk5FhEeGDx0ksvyZw5c2TChAl2Wr9+/eTtt98206eccopMmzZNqlevXnxbCgAAABSS1q9IjAtP6f9DYarbEVVFof7v//5PatSoYf8/ffp0eeutt+TWW2+Vl19+2eRcPP7448W5nQAAAEBUSqUfi/ytWbMmoLjTBx98II0aNZLRo0eb/zdv3izvvPNO8W0lAAAAUAQJctgM4Vp3LPVjUaTAwuPxBPz/1VdfyaWXXmr/37BhQxNcAAAAAOFUKu6wGcK17lhSpKJQzZo1k8mTJ9vFoDZu3Ci9evWyX1+/fr1UqlSp+LYSAAAAgPtyLO677z65/vrrpXLlypKZmWmKRfXs2dN+/ZtvvpF27doV53YCAAAAhZYohyQxTF23JQod5B3TtddeK1WrVjUtP2nOxJ133imlSlmL2rFjh1SpUkVuuumm4t5WAAAAoND1HMLV7GsCzc0WzLnnnmuG3DSomDRpUqjbBQAAACBWet7esGGDvP/++/Liiy+aehUqOzvb5FrouLC0Vak2bdqYWvM6dOnSRb744otQNhEAAAAxzFd5O1xDLIkvaqtQgwcPNk3M3nDDDWZ6+fLl5rV9+/aZVqG0P4vCqlu3rjz99NOyYMECmT9/vpx99tmmtaklS5YUZTMBAAAARHJg8eyzz5pcCq3EPWPGjIDmZ1NSUuTyyy+Xjz/+uNDLvfjii+WCCy6Qpk2bmpannnrqKalQoYLMmzevKJsJAACAGFdasqS0HAzTkGW2gQ7yjuKNN96Qm2++Wf773//K9u3b87yuxZlCLcKkRak+/PBD0+qUFokCAAAACssqkhSeVqFKeYtC0UHeUaxbt066du2a7+vly5eXPXv2FGmDfvvtNxNIHDx40ORWaH8ZGuEBAAAAcFlgUb16dRNc5EfrSNSvX79IG3TSSSfJokWLZPfu3fLRRx9Jnz595NtvvzXBRVZWlhmcsg4dlqTE0kVaFwAAAGKhudnw5FgkxFhzs0WqY6F1KF599VVZtWqVnRYXF2fGX331lYwbN06uuuqqIm1QYmKiNGnSRDp06CDDhw+Xtm3bmvocSv/XOhzO4cV3aDUKAAAAwSVKliTKwTANWRJLihRYPP7441KrVi3Tu7bWtdCgYsSIEXL66adLr169TB2Lf//738WygTk5OXYuxZAhQ0xOhnMYdFOvYlkPAAAAgBIOLDSnQFtqeuCBB0xfFmXKlDHFlXbt2iVDhw6V7777TsqVK1fo5WrgMHv2bPnrr79MXQv9f9asWaZJW5WUlGT3ceEbKAYFAACA/CTEHQ7rUFjacpR23aDP1507d5affvrpqPNrY0fNmzc387du3VqmTZsW8Hrfvn1NJoBzOP/88+V4KHKBs7Jly8rDDz9shuKyZcsWkwOyadMmE7xozsf06dOD9vANAAAAHEspU8ciIWzrLoyJEyea/uG0yoEGFSNHjpSePXvKsmXLTB3n3ObMmSPXXXedqS5w0UUXyfjx46V3796ycOFCadWqlT2fBhJjx461/9cf6yMmx0I7rps5c2a+r6enp5t5CuvNN980uRVa9EmDjK+//pqgAgAAADHhhRdekP79+0u/fv1Mw0UaYGgpoDFjxgSdX+sha9Bw//33S4sWLeTJJ5+U9u3by6hRowLm00CiZs2a9lC5cuXICSy0eNLff/+d7+saFGjRKAAAACCcSskRb65FOIYjBd7OQ4cOmZZVe/ToYafFx8eb/+fOnRv0PZrunF9pDkfu+fXZXXM8tPXVO+64I2g/dGEtCuVrBSqYlStXSsWKFYu6aAAAAMA19uTq301zEHIXR9q2bZvpILpGjRoB6fr/0qVLgy538+bNQefXdB/N0dAWXRs1aiR//vmnaWBJG1vS4CMhISE8gcVbb71lBp9hw4aZHrhz0wrcv/76q1xwwQXFt5UAAABAUewYIXIwTOveb43q1asXkKyNHT322GMlsgnXXnutPa2Vu7UOc+PGjU0uxjnnnBOewGL//v2ydetW+/+9e/ea7JncuRja6/btt98ujz76aLFuKAAAAFBoHu8QrnWLmI6ltTXTo1WePuGEE0wOQu7qBvq/1osIRtMLM7868cQTzbq0hFHYAgstj6WD0qwUrSxyySWXFOvGAAAAAG6T7O0m4VidRGsH0dpAkrbs5OvPTf8fOHBg0Pd06dLFvH7PPffYaTNmzDDp+Vm/fr2pY6F90kVEHYvVq1cX+4YAAAAAxS7HO4Rr3YWgTc326dNHOnbsKJ06dTLNzWZmZppWopR2y1CnTh3TvKwaNGiQdOvWTZ5//nm58MILZcKECTJ//nx5/fXXzev79u0zHVtfccUVJhdD61hoP3RNmjQxlbwjpvK2s0iU9oCtEVVu9evXD3XxAAAAQEwEFtdcc42peqBVCrQCdrt27eTLL7+0K2ivXbs2oCpC165dTd8V2q+cVspu2rSpfPLJJ3YfFlq0Sus+az1prQddu3ZtOe+880yztMejL4siBxajR482be2uWrUq33m0ZjsAAACAgtFiT/kVfdIK17ldddVVZsivQ2vtbLqkFKkfC+2sY8CAASYbRVuH8ng8pmzXQw89ZLJZ2rZtazq7AwAAACIixyJcQwwpUmDx8ssvm3JZX3zxhdx6660mTct1PfXUU/L777+b4lHHq+MNAAAAoNCtQoVriCFFCiy04sfFF19spkuXLm33FqhSUlLkn//8p7zyyivFuZ0AAAAAIliR6lho8HDkiNVFuTadVa5cOdM+r4/2uu3s8Q8AAAAIiyiqvB2TORZa0/yXX36x/z/11FNNZe4NGzaYAOO1116TZs2aFed2AgAAAIVHHYvIzrG48cYbTQXurKws01SVto/bo0cPu3lZLR718ccfF/e2AgAAAHBTYKGddPg66lCnnXaaLFmyRD799FPTXq62j0uOBQAAAMIuAopCpaammmdkbVVVB7cqUmChnXNUq1bNtI3rc+KJJ5re/9SBAwfMPHSQBwAAgFiXkZFh6iW7XZHqWDRq1EgmT56c7+tTp0418wAAAABhRXOzkZ1joR3iHc3hw4cDuhsHAAAAwsITxqJQHokpBQ4s9uzZI7t27bL/1w7wtLhTbjrPhAkTpFatWsW3lQAAAADcEVj873//kyeeeMJMx8XFyT333GOG/HI0hg0bVnxbCQAAAERp5e1YUeDAQlt6qlChggkaHnjgAbnuuuukffv2AfNowFG+fHnp0KGDdOzY8XhsLwAAAFBwBBaRF1h06dLFDCozM1OuuOIK01EeAAAAABSp8vbQoUPzpOXk5Mi2bdtMM7SacwEAAACEHTkWJabATTctX75c3n77bdm5c2dA+u7du+Xmm2+WcuXKmQrbGliMGjXqeGwrAAAAUDg0Nxt5gcXzzz8vjzzyiFSqVCkg/bbbbpN3331XGjRoIJdffrkkJSWZjvI++eST47G9AAAAAKK5KNQPP/wgF110UUAxp3Xr1skHH3xg6l58++23UqpUKdPcrHZbnpaWJr179z5e2w0AAAAcG0WhIi/HYsOGDdK8efOAtM8++8wEGppDoUGF0hwNLRr1888/F//WAgAAAEUJLMI1xJACBxZaObt06dIBad9//70Zd+vWLSC9bt26snfv3uLaRgAAAAARLs6jHVMUgPZZ0aZNGxk3bpz5Pzs72wQQVapUkSVLluSpj6Ed6q1fv16Ot3OunmPGh7OO2GmlSid4t9EfJnpy/Lt52c1WHxu/L9pop21ca1VKL18xyU5r1KyaPb1wzl9m3L5LAzttyc/W+w8eOOxfdykrVqtUpZydtm9vlj19cvs6Zrx+9Q47bffOA3bw5nPW+f7codlfLc/zemnvPu7YmmmnVaxUxoxr1fXXg0mpXNae9u1b+ud/2GnjH9pkvTfOvz2TDtyR57iVLXXITtu2y0pLTPKXpLtjV6I1scBOEpnrmB5ijVZV/shO+junvhl3WdDJP5//8Is85zsYjrSl3vFmf9J346zcsbXZ/mP2x9I99nStuilmPGC4dXyMq7xj69Bb7+/2vj1d/5vrrAlnA2gPeMf7HGmNvWPrtBrp18hRdW/knajpSHTsT/rqXPMp6/ILzrddaotjurJ3eS841u0tyZjuuOsD1uNzgWN6Yd5ttLfH2Q/mSY7p573rmZt33U7O7Sis7i/5px9N9O/4E79Vt5adVvRlB6zH6hfU4r0NvjvXnyN7xven+F/3XnbpI46xzLhiOgZWC+AWa7dFpjrSGgZeU8Up4Lqpmfd8F2U5R9vOgOvnVMd0X+94nWM5x7F/1uI6dwVe32DH+l4o+vE71vIDPicaHeNzx3f8nYUYZgV5T8O829bdcV+mD8h7TNd+Z30O13/f+xms9gdZ97Yg1771tWh5Mu+6g907ox/2f7fdMTDx6Pt9pzXKvN77ASciA6dYH5Y5jmeMtxJa5vkcCLhX/xH4Ga3erGR9v9cq5b+QP5np37Zy5a1tW/ab9Z2tGja1nlFGt/F9ETm+0/yPHSKr/JNvnGE9K33+wS95ng3WrPQf1IH/8vdTdu3ZydbEZMcy13rHVSXPeVrefIqdtD2nlj1dI95606QNPe209GnW88iBTP++VqlewZ5O8j5nbFy7y06r6f1Ob9DkBDvt6Ybe9VwceFPu2bNHUlJSZPdzIsn+x6ESteeASMp9VmNHycneY+liBa5j0adPH7n//vulRYsW0rVrV3nvvfdky5Ytcvfdd+eZ97vvvpNmzZoV97YCAAAAiPbA4s4775Svv/5ahgwZYupVaEaHFoG67777AubTCt1ffPGFDBt2HH8yAgAAAAoinM2+eiSmFDiw0PoVn376qcyfP1/+/PNP07zsqac686MtWVlZMn78eDnzzDOLe1sBAACAqGsVKjU1VRISEmTAgAFmcKtC97zdsWNHM+SnSZMmZgAAAAAgkpGRQR0LAAAAIKp5wphj4ZGYQmABAAAA94qAolCxosD9WAAAAABAfsixAAAAgHuRY1FiCCwAAADgXjQ3W2IoCgUAAACg5HIsgvWwfTTaid6LL75YlG0CAAAAigdFoSIvsBg1apTd43ZBEFgAAAAAsaPARaGSkpLMcOWVV8qkSZNMD9s5OTn5DtnZ2cd3ywEAAICC5liEa4ghBQ4stmzZIqNHj5bdu3fLVVddJTVq1JD+/ftLenr68d1CAAAAoKgILCIvsKhYsaL06dNHpk+fLuvXr5ehQ4fKr7/+Kuecc47UqVNHBg8eLPPnzz++WwsAAADAPa1CaW7FoEGD5Mcff5QVK1bIbbfdJtOmTZPOnTvLM888U/xbCQAAAITS3Gy4hhgScj8Wu3btMsWj9u7dayp2ly1btni2DAAAAAgVrUJFdmCxfPlyGT9+vLz//vsmx6J58+Zyxx13yPXXXy8nnnhi8W8lAAAAAHcEFhs2bDCBhA4///yz1K1bV6699loTTLRr1+74biUAAABQFORYRF5gUb9+fSlTpoxccMEF8vDDD8uZZ55p+qpQO3bsCPqeKlWqFN+WAgAAAIXlCeMDvkdiSoEDC60/ceDAAdOHhQ4FQV8WAAAAQGwocGChzcsCAAAAUYWiUCWGwAIAAADuFc5mXz0SU4rUjwUAAAAAFCnHYuHChVJY7du3L/R7AAAAgGJDUajICyw6duxotwJVkIreOi+VtwEAAIDYUKgO8rS52QsvvFB69uwppUqF3Gn3UT399NMyZMgQGTRokIwcOfK4rgsAAAAuFQE5FqmpqZKQkCADBgwwg1sVODp47bXXTG/b2tTsrFmz5MorrzSd451++unFvlEZGRlmfW3atCn2ZQMAACCGREBgkZGRIcnJyeJ2Ba683b9/f0lPT5c1a9bI/fffL/PmzTOd5DVs2NDkLPz666/FskH79u2TG264Qd544w2pXLlysSwTAAAAQIS1ClWnTh0TWGhl7iVLlsiNN94oH3zwgZxyyinSunVrmT59ekgbpNlDWtyqR48eIS0HAAAAsJubDdcQQ0JqbrZFixYybNgwmTx5snTr1s0EGj/++GORlzdhwgQTsAwfPjyUzQIAAAACi0KFa4ghRa6BvXr1ann//ffN8Pvvv8uJJ54oDz/8sPTt27dIy1u3bp2pqD1jxgxTSTyYrKwsMzjlZB+S+ITEIq0TAAAAQBgCiy1btsjEiRNNJW7NmahZs6ZcffXV8uabb0qnTp1C2pAFCxaY5Tv7vtDmamfPni2jRo0yAYXmZDz++OMB72vY8h9y4sm3hLRuAAAAuFQEVN6OFQUOLM477zxTebtChQpy+eWXy5NPPilnn322xMcXT+fd55xzjvz2228Baf369ZPmzZvLgw8+aJro0krigwcPDpjn0n6F77gPAAAAMYLAIvICi6+//lrKli1r2uHdunWrvPTSS2bIj3aQN2XKlAJvSMWKFaVVq1YBaeXLl5eqVava6UlJSWZwohgUAAAAEEWBRf369U2wsGLFigLNX9BeugEAAIDjxhPGnAOPxJQCBxZ//fWXlDTtiA8AAAAosnA2++qRmFI8FSQAAAAAxLQiNzcLAAAARDwqb0deYNGmTZtCLVjrWPzyyy9F2SYAAAAAbg0sqlSpElAh+/DhwzJnzhwTcFSuXPl4bR8AAABQdORYRF5gkbsi9bZt26R69erywgsvmP4sAAAAgIhzJIy1io9ITCnyYaY5WQAAAAA+VN4GAACAe1EUqsQQWAAAAMC9KApVYujHAgAAAEDJ5VgsXLgw4P/du3eb8YoVK6RSpUpB39O+fftQtw8AAAAouuww5hxkS0wpcGDRsWPHoBW277zzzjxpHo/HzJudHWNHEwAAAJFFg4pwtTl0RGJKgQOLsWPHHt8tAQAAAOD+wKJPnz7Hd0sAAACA4kaORYmh8jYAAACAkNHcLAAAANyLfixKDIEFAAAA3IuiUCWGolAAAAAAQkaOBQAAANwrO4w5FtkSUwgsAAAA4F7hLI50RGIKRaEAAAAAhIwcCwAAALgXrUKVGAILAAAAuBdFoUoMRaEAAAAAhIwcCwAAALgXORYlhhwLAAAAuFe29wE/HEN24Tc3LS1NGjZsKGXKlJHOnTvLTz/9dNT5P/zwQ2nevLmZv3Xr1jJt2rSA1ydNmiTnnXeeVK1aVeLi4mTRokVyvBBYAAAAABFg4sSJMnjwYBk6dKgsXLhQ2rZtKz179pQtW7YEnX/OnDly3XXXyS233CI///yz9O7d2wyLFy+258nMzJTTTz9dRowYcdy3n8ACAAAA7nUkzEMhvPDCC9K/f3/p16+ftGzZUl599VUpV66cjBkzJuj8L774opx//vly//33S4sWLeTJJ5+U9u3by6hRo+x5brrpJnn00UelR48ecrwRWAAAAABhdujQIVmwYEFAABAfH2/+nzt3btD3aHrugEFzOPKb/3ij8jYAAADcS/uSiAvjukVkz549AclJSUlmcNq2bZtkZ2dLjRo1AtL1/6VLlwZd/ObNm4POr+nhEPWBRdlyiXnSatZJMeOsLH/+U0qlsvb0vfeXN+O7/rnaTjuQeciMjxz217KZ8/UKe7p+46pm3LxNbTutVOkEM/7zD3+5t717Dlrzta1lp309ZYk9/dv8dWZcroL/Ytq1PdOMa3i3W82Y6n9P8zbWspYs3GCnHS5lbedZFzS306pUq2DGK5b4L6Zf56+3pytUtNaZmOQ/7TMOXmPG1RL877nlV39G1tRTNprxtpyadlqNyvvMuHfSS3aa/OUdT/YnyWWO6R3W6MTJV9pJa8Za43THbN2PdeO3944r+5POGHdKwDqMKv7J9Du9E/0cr//tHTvu0/plr/O/51Hv9jzoeE+yNdp6yet2UrVlt1rzX1OIffBx3PPp/ksxuIa5jrND+ojg6073BJk3SFrAcanrne8fUjD/kcK7xD/ZferRty3osfQdi0H+pCekeoFWnd/x6X6pN21K3vf4roUAw7zXnGoUZD2NjnFu73TMuz7/dQddpvMamHeM4+e9bbv/lc9+NyrY9ec7bs73BrznWNfvURzz2vfN59xu5wvLvOM/877nWPdDwLwPeyecXyn1g3Rwtcrxnpl5z4N9bh2fUenDjn4efdvmvG6mjrE+e+Vn/3dO98GO9Uwu3PHLvXzb/6zR6pmOnTzH+g7o3uUYCxx3jOvvPv/09OxNZry4TiU7bcto60Gr+9v+h6I/T7c+h+s77/1THdO+49rZkbbLu74vvreTDp/s//6v1cta54cnt/O/5zlrdMdMxwl/z7rZvzvs//I6Y4r/Xh99jvWcsOIH/4d36pnWtpcpW9pOW1B6trU9z5zpX7bzGnneWs/anBZ2Usvs+Wbc5axOdtrG6f7nkYRS1jkZuaWBfzlbveMr8n4+PjNst500e91ye/qk36zz0KhZNTvthBrWs8N7Z7Wy09aWft+/zAnWaGe1NDtpceWuZjxpnvVspH6et8aMj0zxb/cJNaznJNXu1G5mnH1kv522Z+cBM65V339d5OT4L6a9u61nqrMv9B+r25t/bMZDPj/LTnsqecfRv5L0cjjGZ8Bxk22N6tWrF5CsdSgee+wxcZuoDywAAACASLZu3TpJTk4OyLHI7YQTTpCEhAT5+2/fr58W/b9mTf8PvE6aXpj5jzfqWAAAAMC9IqDydnJycsAQLLBITEyUDh06yMyZMx05ODnm/y5dgmcharpzfjVjxox85z/eyLEAAACAe2WHsShUTuFm16Zm+/TpIx07dpROnTrJyJEjTXOx2kqUuvnmm6VOnToyfPhw8/+gQYOkW7du8vzzz8uFF14oEyZMkPnz58vrr/uLbe/YsUPWrl0rGzdaRSyXLbPKkGquRnHnbBBYAAAAABHgmmuuka1bt5rmYbUCdrt27eTLL7+0K2hrgKAtRfl07dpVxo8fLw8//LD8+9//lqZNm8onn3wirVr568xMnTrVDkzUtddee9zqeRBYAAAAwL1yomvdAwcONEMws2bNypN21VVXmSE/ffv2NUNJoI4FAAAA3CsC6likpqaaDu/S0vyta7kRORYAAADAcZSRkRHQKpRbEVgAAADAvY6EsYxOjsQUAgsAAAC4F4FFiaGOBQAAAICQkWMBAAAAd/djEa6cA4/EFHIsAAAAgOMolVahAAAAgCinuRVx4c2xyKBVKAAAAMAFlbfDHFjECopCAQAAAAgZORYAAABwL3IsSgyBBQAAANyLwKLEUBQKAAAAQMjIsQAAAIB7RUCrULGCHAsAAAC4uiTUEU+YBrHQjwUAAACAkGXQjwUAAAAQ3Q57h3CtO5ZQFAoAAABAyMixAAAAgGsd8g7hWncsIbAAAACAax0JY5GkIxJbKAoFAAAAwF2BxWOPPSZxcXEBQ/PmzcO9WQAAAIjibizCOcSSiCsKdfLJJ8vXX39t/1+qVMRtIgAAAKIEdSxiNMfCF0jUrFnTHk444YRwbxIAAABQZKl0kBceK1askNq1a0uZMmWkS5cuMnz4cKlfv364NwsAAABRKBL6scigg7yS17lzZxk3bpycdNJJsmnTJnn88cfljDPOkMWLF0vFihXDvXkAAACIMrQKFaOBRa9evezpNm3amECjQYMG8sEHH8gtt9wiWVlZZnDKzj4kCQmJYdhaAAAAABFbx8KpUqVK0qxZM1m5cqX5X4tFpaSkBAx//jYu3JsJAACACJUd5iGWRHRgsW/fPvnzzz+lVq1a5v8hQ4bI7t27A4bGrfuGezMBAAAQ4XUswjXEkogqCnXffffJxRdfbIo/bdy4UYYOHSoJCQly3XXXmdeTkpLM4EQxKAAAACD8IiqwWL9+vQkitm/fLtWqVZPTTz9d5s2bZ6YBAACAwqIfixgNLCZMmBDuTQAAAAAQ7YEFAAAAUJxobrbkEFgAAADAtSKhg7xYEdGtQgEAAADRLjU1VVq2bClpaWniZuRYAAAAwLXC2Z9EtneckZEhycnJ4nYEFgAAAHAtikKVHIpCAQAAAAgZORYAAABwLVqFKjkEFgAAAHCtQ2F84D0ksYWiUAAAAABCRo4FAAAAXCvHO4Rr3bGEwAIAAACuRVGokkNRKAAAAAAhI8cCAAAArkWrUCWHHAsAAAAAISPHAgAAAK51OIwPvIcltpBjAQAAANc6FOZBpaamSsuWLSUtLU3cjBwLAAAA4DjKyMiQ5ORkcTsCCwAAALgW/ViUHAILAAAAuBatQpUc6lgAAAAACBk5FgAAAHCtQ2H8Jf2QxBYCCwAAALiWFoNKCOO6YwlFoQAAAACEjBwLAAAAuFa2dwjXumMJORYAAAAAQkaOBQAAAFyL5mZLDoEFAAAAXOtIGCtvH5HYQlEoAAAAACGL83g8HoliZ1wy24zbpNaz01Yt22LGhw/7O1LvfUN7e3rgvjJm/Nj+rXbavPQ/zTipjD8T51CWP87cu/ugGZcq7Y95jxy2quTUO7Gqndaw6Qlm/PPcNXZafEKcPV2zTooZt+1U307bunmvGa9e7t+exET/evZ41/2/m5baaRPWXmjGm9fvstP++GWjGVesVNZOyzrgz/zrfWMHM57zzUr/exZtyLMPLdrUsqdnfva7GXfo2tBO27kt0zoWif5jtW+PtY0JCf5YtW6jKva0J8c6F0llSttp5SskmfEJNSvaaeeWmWhPj5xzmhlXqlLOTvt9kbWPB/b7W4Y+oYb1/vZdGthpO7Za26gyM615F/yw2k47uN86Luf1bmWnOc/tkORK3h2zk0Ruskbpjjumu+/UXuKY7xz/5Mv1D5jxJ+8tzLPftep51yEiK5Zstqd918aJJ1Wz0y494RMzfv+vC/z7tS8rz/FZ6LjufOckpbL/eijnXbfz2t64dqd/mXutZTZr5b8GylVINOPtW/bluc4bN6/uny/e//rXM6z9ycryX39bN1nXeUIp/zXS3HGtPd/Aew/7b1uRbdbou1Y/20kvvGudgFM6+++hR3+3tkelv2CNu7/mX8zUZtZ189k3/utih/c6Voe9x2PK6R39b7rFO37VsT2+S/oPR5r/1hHZ5N2GYf6k7o28E0P9aa9W91+/t2+wjq9Yh8difRxJelre5aT7L2Pp3s8/nT421/qc/nKs+/NDec73v/7234MyyDs+1bHsuXkXaV/7jnvCmZb7tTzvH+edeNwxr2PfCqp7F++E/xYSuTrIjOUd00OPci87+T727nWkLQsyn+Oef+6QdSLvO1Ixz2eHWvCt9Z310S9N7bTEJOuz9PHy/nteHvaOb3CsZ79j+n/ecV9H2izv+N/+pMfE/70ya9rSPPdtlWrWgSlbznsdikiDJtb9dMsyx2+P7zrW4/s6fcWR9o53XMGfNDLbv8F/rbC2Y/Dpc+y0+kuusyb8Xwsi3+Xal9zXfFz+11X3Sx3vmZL39WDzBpsv6LXgvM6d99jQIJ8J3v356JztdtKV8x0fFL7jVtPxnnlBVui4N8R3DHb4k/7RwjqfOdn+gzFufwtrorLjvdZjhyXZO/Z//In8K+/32NzBP/mXOdP64HM+Lm5caz17nHNxSzvt3p3WtTS1ifV5q/7c7l95ae937K4dzuvC+pBf+qv/PRVTrGc0VT7Zmt67y/oudV6f9R3PLbO+sI7F1xN9HwiWPXv2SEpKivlYs779Sl6WiLwoIrt375bkZN8JcC+KQgEAAMC1NCTyxMWFZ92eqP79vtAoCgUAAAAgZAQWAAAAcHeORRgHlZqaKi1btpS0NEcZVxeiKBQAAABcy/mAH451q4yMjJioY0GOBQAAAICQkWMBAAAA18qJs4awrFvCmF0SBgQWAAAAcK1IKAoVKygKBQAAACBk5FgAAADAtcixKDnkWAAAAAAIGTkWAAAAcC2PxJkhPOuOrXwLAgsAAAC4lieMrUJ57D+xgaJQAAAAAEJGjgUAAABci8rbJYfAAgAAAK5FYFFyKAoFAAAAIGTkWAAAAMC1PHFxZgjLusX/NxYQWAAAAMC1crxDuNYdSygKBQAAACBk5FgAAADAtai8XXIILAAAAODqDvJ0CMu6JbZQFAoAAABAyMixAAAAgGtRFKrkkGMBAAAAIGTkWAAAAMC1ciTODOFZd2whsAAAAIBrUXk7hotCbdiwQW688UapWrWqlC1bVlq3bi3z588P92YBAAAARZKamiotW7aUtLQ0cbOIyrHYuXOnnHbaadK9e3f54osvpFq1arJixQqpXLlyuDcNAAAAUSgSKm9nZGRIcnKyuF1EBRYjRoyQevXqydixY+20Ro0ahXWbAAAAEL1ywljXIUdiS0QVhZo6dap07NhRrrrqKqlevbqccsop8sYbb4R7swAAAABEU2CxatUqGT16tDRt2lSmT58ud9xxh9x9993y1ltvhXvTAAAAAERLUaicnByTY/Hf//7X/K85FosXL5ZXX31V+vTpI1lZWWYIeE/2IYlPSAzTFgMAACCS5cTFmSEs65bYElE5FrVq1TI15p1atGgha9euNdPDhw+XlJSUgGHdyvfCtLUAAAAAIjKw0Bahli1bFpC2fPlyadCggZkeMmSI7N69O2Co1+SGMG0tAAAAoqllqJIeYk1EFYW69957pWvXrqYo1NVXXy0//fSTvP7662ZQSUlJZnCiGBQAAAAQfvGR1nnI5MmT5f3335dWrVrJk08+KSNHjpQbbiBXAgAAAEVvbjZcQyyJqBwLddFFF5kBAAAACJnW2w5P3e2YE1E5FgAAAACiU8TlWAAAAADFhZ63Sw6BBQAAAFzLI3FmCM+6YwtFoQAAAACEjBwLAAAAuFZOnDWEZd0SWwgsAAAA4Frh7KzOI7GFolAAAAAAQkaOBQAAAFyLHIuSQ2ABAAAA16JVqJJDUSgAAAAAISPHAgAAAK5Fq1AlhxwLAAAAACEjxwIAAACuReXtkkNgAQAAANcisCg5FIUCAAAAEDJyLAAAAOBanjhrCMu6JbYQWAAAAMC1ciTODOFZd2yhKBQAAACAkJFjAQAAANei8nbJIbAAAACAe4WxjkWsoSgUAAAAgJCRYwEAAADXoihUySGwAAAAgGsRWJQcikIBAAAACBmBBQAAAFzfj0W4hsJKS0uThg0bSpkyZaRz587y008/HXX+Dz/8UJo3b27mb926tUybNi3gdY/HI48++qjUqlVLypYtKz169JAVK1YEzPPUU09J165dpVy5clKpUiUpKgILAAAAIAJMnDhRBg8eLEOHDpWFCxdK27ZtpWfPnrJly5ag88+ZM0euu+46ueWWW+Tnn3+W3r17m2Hx4sX2PM8884y89NJL8uqrr8qPP/4o5cuXN8s8ePCgPc+hQ4fkqquukjvuuCOk7SewAAAAgGtpU7PhHArjhRdekP79+0u/fv2kZcuWJhjQXIQxY8YEnf/FF1+U888/X+6//35p0aKFPPnkk9K+fXsZNWqUnVsxcuRIefjhh+XSSy+VNm3ayNtvvy0bN26UTz75xF7O448/Lvfee6/J8QgFgQUAAABcX3k7XENBaa7BggULTFEln/j4ePP/3Llzg75H053zK82N8M2/evVq2bx5c8A8KSkppohVfssMBa1CAQAAwLWOeMLXPtMR72r37NkTkJ6UlGQGp23btkl2drbUqFEjIF3/X7p0adDla9AQbH5N973uS8tvnmLliXIHDx70DB061IxjSazut1OsH4NY338V68cg1vdfxfoxiPX9VxwDjkF++3/gwAFPzZo1w51h4alQoUKeNN3e3DZs2GBemzNnTkD6/fff7+nUqVPQfS9durRn/PjxAWlpaWme6tWrm+kffvjBLHPjxo0B81x11VWeq6++Os/yxo4d60lJSfEUVZz+kSimEaBm6ezevVuSk5MlVsTqfjvF+jGI9f1XsX4MYn3/Vawfg1jff8Ux4Bgcbf+1grIWMQonj8cjcXFxx8yx0O3U+hQfffSRqYDt06dPH9m1a5dMmTIlz7Lr169vKnvfc889dppW/Nb6E7/88ousWrVKGjdubCp2t2vXzp6nW7du5n+to+E0btw4syxdX1FQFAoAAACupE2w6hANEhMTpUOHDjJz5kw7sMjJyTH/Dxw4MOh7unTpYl53BhYzZsww6apRo0ZSs2ZNM48vsNBATFuHCrUFqGAILAAAAIAIMHjwYJND0bFjR+nUqZNp0SkzM9O0EqVuvvlmqVOnjgwfPtz8P2jQIJP78Pzzz8uFF14oEyZMkPnz58vrr79uXtecEg06hg0bJk2bNjWBxiOPPCK1a9cOyBVZu3at7Nixw4y1nseiRYtMepMmTaRChQoF3n4CCwAAACACXHPNNbJ161bToZ1WrtZchi+//NKufK0P/tpSlI92ajd+/HjTnOy///1vEzxoMahWrVrZ8zzwwAMmOLn11ltNEafTTz/dLNOZk6Pre+utt+z/TznlFDNOT0+Xs846K3YCCy2fpmXJcpdTc7tY3W+nWD8Gsb7/KtaPQazvv4r1YxDr+684BhwDt+3/wIED8y36NGvWrDxp2rGdDvnRXIsnnnjCDPnRuhU6hCrqK28DAAAACD86yAMAAAAQMgILAAAAACEjsAAAAAAQsqgPLNLS0qRhw4amZnvnzp3lp59+ErfQpsRSU1OlYsWKUr16ddMs2LJly4LOq1VlevXqZSroaGsAbvT000/bzab5/Pnnn3LZZZdJtWrVTKc4V199tfz999/iJnp9637nHgYMGBAz18DevXvNeW/QoIGULVvWtIKRkZFhv67nvG/fvqb5PO1c6Pzzz5cVK1ZItJo9e7ZcfPHFZn9yn8/Dhw/Lgw8+KK1bt5by5cubebT5wY0bNwZdVlZWlmlVRJfjaz4wmvc/t9tvv93Mo00yOi1cuFDOPfdcqVSpklStWtW0hrJv3z6JFsc6BsE+E3R49tlnY+IaKMg9H83fD8f6/tdmQe+66y456aSTzGeidpJ29913mw7igtm+fbvUrVvXHMuidnwWrc9A2n+DfmfocrQ/B/38PHLkSAntReyJ6sBi4sSJpr1fbQlAv0Tatm0rPXv2lC1btogbfPvtt+bhcd68eaazE32gOO+880yTYbnpl2ruXh3dRB8iX3vtNWnTpo2dpsdBj4fu9zfffCM//PCD6bVSv4y0Qxk37fumTZvsQa8FlbsFCDdfA//85z/Nfr/zzjvy22+/mfPeo0cP2bBhg/lC0S8c7V1UeyXV3kU1ANHXg90r0UC3Wz/P9IeT3Pbv328+77Qdch1PmjTJfNlecsklQZelzQzqw5db9t9p8uTJ5vMx9/5pkKXnX9tf106gtFnFJUuWmAdRtxwD52eCDmPGjDH3/xVXXOH6a6Ag93y0fz8c6/tfr3EdnnvuOVm8eLFpzUev81tuuSXo8jTd+f0ZK89A2vP0BRdcYAJPvU70uXHq1Kny0EMPldBexCBPFOvUqZNnwIAB9v/Z2dme2rVre4YPH+5xoy1btmgLXp5vv/02IP3nn3/21KlTx7Np0ybz+uTJkz1usnfvXk/Tpk09M2bM8HTr1s0zaNAgkz59+nRPfHy8Z/fu3fa8u3bt8sTFxZl53Ur3v3Hjxp6cnJyYuAb279/vSUhI8Hz22WcB6e3bt/f85z//8Sxbtszs8+LFiwM+C6pVq+Z54403PNGuIOfzp59+MvOtWbMmIH3atGme5s2be5YsWWJe1+vELfu/fv16c83reW/QoIHnf//7n/3aa6+95qlevbq5Dnx+/fVXs6wVK1Z43HgNXHrppZ6zzz47T7obr4GC3PNu+37I7/vf6YMPPvAkJiZ6Dh8+HJD+yiuvmO/OmTNnmmXs3LnTEyvPQEOGDPF07NgxYP6pU6d6ypQp49mzZ0+JbXssidocC/3lYcGCBeYXCh/tMET/nzt3rriRL4uzSpUqAb9eXn/99eZXHc3icyP9xUJ7k3Sea1/2vv5C4Wy3WovE6XXw/fffixvpdf/uu+/KP/7xD/vXGbdfA5plrb2AOjvyUZr9r+dZrwPlfF2vAb0u3HodBPts0OtBi/34aJGP/v37m1weLSriJvqL80033ST333+/nHzyyXle12siMTExoBMpvV6UG68JPdeff/55nl+r3XoNFOSed9v3Q7Dv/2DzaJGvUqX8XZT9/vvvpu+Ct99+O+B+iJVnIL0Ogn13HDx40DxDovhF7VW2bds287Dh64nQR//XngrdRr9ItYz5aaedFtCb4r333mvKDl566aXiRto1vRb38HVd73TqqaeaMuZaXlI/XDR79L777jPXhRYNcCMtO6rlY51FOtx+DWi52C5dusiTTz5psv71/GpwpT8g6Hlu3ry5KV88ZMgQ2blzpwm+RowYIevXr3ftdeCkX5B6D1x33XXmoULpj7x6jWj9g44dO4rb6PnVhyctUx7M2Wefbb4HtL6BXg96XfiKPrjxmtDecvU+ufzyy+00N18DBbnn3fT9kN/3f+5nIv2M1LpEzodq/VzQ+0CPVyw+A2nx+Dlz5sj7779vzr0Wn/V1Ehdt10G0iNrAItbor/ZajlIftH20nKCWHc1dadEt1q1bJ4MGDZL33nsvzy8OSivkffjhh/Lpp59KhQoVJCUlxTx0t2/fPup/mcnPm2++aSqo+cpLu/0a8NFfXPVBqU6dOuYXyJdeesl8Yep5Ll26tKlnsHz5cvNLlv4ym56ebo6TW68DHy1zrBVS9diMHj3aTn/55ZdNhXd98HIb/ZXxxRdfNGXK86tTpLkY+rD9/PPPm+tBf8ls1KiR+eHJjdeE1q+44YYbAj4n3XwNFOSed9P3Q7Dvf6c9e/aYXP2WLVvKY489ZqfruW/RooXceOONEqvPQFonQwMrDbD1u6NZs2amzoWKtusganiiVFZWlil3nbvc6c033+y55JJLPG6i9Ujq1q3rWbVqVZ6y9lpeVI+Db9BTquVKtTxltNNzq/uTe/98+3zkyBF73q1bt9rlRmvUqOF55plnPG7z119/mXP7ySefxMw1kNu+ffs8GzduNNNXX32154ILLgh4XctQazlcXx2sO++80xPt8itff+jQIU/v3r09bdq08Wzbti1PeXu9BnJfFzrWz8ho3n+tS5HfNa91LXLbvHmzqael147Oo+XQ3VTHYvbs2eb1RYsWxcw1UNh7Ppq/H/L7/vfRegJdunTxnHPOOZ4DBw4EvNa2bduAa0CnfdfAo48+6omlZyCtk7hhwwZTZ+/3338382ndNBS/qA0sfB8iAwcODKi8pRV43FJ5W28EvaG0Qvry5cvzvK4VlX777beAQW+WF198Md8PoWiiH5i5908rYd14441mOhitnKYfNEuXLvW4zdChQz01a9YMqJjn9msgPzt27PCkpKSYSrrB6P2iXy5agTPaBXuo8gUVJ598sv1Q5aSVuJ3XhB4HXc5HH33kWbdunSea91+DqNzXvH5GPvjgg0e97998801PuXLlorLi6tEerPv06ePp0KFDTF0DRb3no+n74Vjf/0orpp966qnmITozMzPP6ytXrgy4BsaMGWOO5Zw5czx///23J1afgR555BFPvXr1An6cRPHx1/CJQtrUbJ8+fUz50U6dOpnsMC1H2a9fP3EDzfobP368aU5Py8/66o5olq5WPtLs/WCVlbQspWb7Rzvd59zlSbXMrLZJ70sfO3asyerVbG8tc69Fp7TMpbbt7SZavlT3Va93Z8U8t18DPtOnTzfFffS8rly50lTa1XLWvntdizzoNaD7rc3R6nWgzVFqNng00v4WdD99Vq9ebfof0GIftWrVkiuvvNLUPfrss89MuWHfZ4O+rpWWc5en1qIgqnHjxqYt+2jef903/QzIXTRG7wPnfT9q1ChT9lr3XZuq1GtG+8JxVnCP5mPgKwKj174W+crN7ddAQe75aP5+ONb3v5573VetP6J1zvR/HZTub0JCgjnXuethKD0m0XAfFNczkBaF0uZmteiTFqHTz4EPPvjAHCMcB54o9/LLL3vq169vmljTHIx58+Z53EJPT7Bh7NixR32Pm5oazc3Z3KzSXyk1a7t06dKmSdrnn38+oBlWt/D92qjNLB6LG6+BiRMnek488URzn2uujf6KpUUgfPQXKs0q1+tAPw8efvhhU1wyWqWnpwe99/XX6dWrV+f72aDvC8b3nmhpavRo+x9M7uZm1U033eSpUqWKuWa0uNjbb7/tiSYFOQaaY1e2bNmAeyE/brsGCnLPR/P3w7G+//M7PjrouQ7G955oybUrrmeg7t27mxxubWK2c+fOpglmHD9x+ud4BCwAAAAAYgdV4gEAAACEjMACAAAAQMgILAAAAACEjMACAAAAQMgILAAAAACEjMACAAAAQMgILAAAAACEjMACAAAAQMgILACgEPr27SsNGzYUN4uFfQQAFD8CCwAxLy4urkDDrFmzwr2pEW/atGny2GOPhXszAABhEOfxeDzhWDEARIp333034P+3335bZsyYIe+8805A+rnnnitVqlSRnJwcSUpKErc6fPhwkfdx4MCBkpaWJny1AEDsKRXuDQCAcLvxxhsD/p83b54JLHKnx4rSpUuHexMAAFGIolAAEEL9g7/++ssUk3ruuefML/UnnniilCtXTs477zxZt26d+eX+ySeflLp160rZsmXl0ksvlR07duRZ7hdffCFnnHGGlC9fXipWrCgXXnihLFmy5JjbM27cOLP+2bNny2233SZVq1aV5ORkufnmm2Xnzp155n/llVfk5JNPNrkRtWvXlgEDBsiuXbsKvI+vv/66NG7c2Lw/NTVVMjIyAt6nx0A5i5D5TJgwQTp06GD2T7exdevW8uKLLxbouAMAIh85FgBQDN577z05dOiQ3HXXXSZweOaZZ+Tqq6+Ws88+29TNePDBB2XlypXy8ssvy3333Sdjxoyx36tFrvr06SM9e/aUESNGyP79+2X06NFy+umny88//1ygitRaBKlSpUqmfsOyZcvM+9esWWPW7Xu419cef/xx6dGjh9xxxx32fBoc/PDDD8fMqRg/frzs3bvXBDC6TN3Hyy+/XFatWmXeq+kbN24MWoxM06677jo555xzzD6qP/74w6x30KBBRTzqAIBIQmABAMVgw4YNsmLFCklJSTH/Z2dny/Dhw+XAgQMyf/58KVXK+rjdunWrCUL0gV5/9d+3b5/cfffd8s9//tPkBvhooHHSSSfJf//734D0/CQmJsrMmTPt4KBBgwbywAMPyKeffiqXXHKJWa9uj+akaO5IfLyVYd28eXMTlGg9k379+h11HWvXrjX7WLlyZfO/bp/mwEyfPl0uuugi6dKlizRr1ixoMbLPP//c5FLovAkJCYU+vgCAyEdRKAAoBldddZUdVKjOnTubsT5g+4IKX7rmbGggovQhXIsi6a/527Ztswd9+NZ509PTC7T+W2+9NSDHQXMkdL3aSpP6+uuvzXrvueceO6hQ/fv3Nw/8+uB/LNdcc40dVCgtuqU0x+JYNDclMzPT7C8AwJ3IsQCAYlC/fv2A/31BRr169YKm++o/aA6A0iJTwehDf0E0bdo04P8KFSpIrVq1TP0IpcWifLkMuXM6tF6I7/XC7KMvyAhWlyO3O++8Uz744APp1auX1KlTx+ScaFGx888/vwB7BwCIBgQWAFAM8ivek1+6rzlWbdZVaZ2EmjVr5pnPmdsRbsfal6OpXr26LFq0yBSF0qJYOowdO9ZUMn/rrbeOw9YCAEpa5HxjAUAM0haWfA/eWqm6qDTno3v37vb/Wndj06ZNcsEFF9h1LpRW2NYcCh8tHrV69eqQ1u3kbAUqN80dufjii82gAZXmYrz22mvyyCOPSJMmTYpl/QCA8KGOBQCEkbYEpcWdtJK2dkyXm1a6Lgit4O18v1YOP3LkiCl6pDRw0Af7l156KSCH4c0335Tdu3eb5m2LgzaXq3I3Ybt9+/aA/7WeR5s2bcx0VlZWsawbABBe5FgAQBhpUKFBwE033STt27eXa6+9VqpVq2ZaYNIK1aeddpqMGjXqmMvRnAdtylXrLWiuhPZXoc3VaotQSpc5ZMgQ09ys1mvQdN982h9FcXUGqP1UKG3pSoMmLT6l+6StXmkzvFqXRPv00Dod2vRuu3btpEWLFsWybgBAeBFYAECYXX/99aazuqefflqeffZZ8wu+VnDWVpeO1QSsjwYf2ozto48+anIutJUpzZ1wFk3Sfiw0wNB57733XqlSpYppTUpzS4qrt23t10L78tDO8LQJW80d0cBCAxfNVdFARnMztD6JtjKl2+RspQoAEL3iPAWpdQcAiEja87YGH9rJXceOHcO9OQCAGMbPRAAAAABCRmABAAAAIGQEFgAAAABCRh0LAAAAACEjxwIAAABAyAgsAAAAAISMwAIAAABAyAgsAAAAAISMwAIAAABAyAgsAAAAAISMwAIAAABAyAgsAAAAAISMwAIAAACAhOr/AVarynSa16yuAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "alpha = 0.05 # threshold for p-values\n", "pval_corrected, rejected_corrected =statistics.pval_correction(result_univariate, \n", " method='fdr_bh', alpha=0.5)\n", "# Plot p-values\n", "graphics.plot_p_value_matrix(pval_corrected.T, \n", " title_text =\"Univaraite - Benjamini/Hochberg\",\n", " figsize=(8, 5), \n", " xlabel=\"Time points\", \n", " ylabel=\"HMM States\", \n", " annot=False, \n", " num_x_ticks=11)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Cluster based permutation testing**\\\n", "In order to provide a more strict control over Type I mistakes, we can also apply cluster-based permutation testing to control the Family-Wise Error Rate when conducting multiple comparisons." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pval_cluster =statistics.pval_cluster_based_correction(result_univariate, individual_feature=True)\n", "# Plot p-values\n", "graphics.plot_p_value_matrix(pval_cluster.T, \n", " title_text =\"Univariate - Cluster corrected\",\n", " figsize=(8, 5), \n", " xlabel=\"Time points\", \n", " ylabel=\"HMM States\", \n", " annot=False, \n", " num_x_ticks=11)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Family-Wise Error Rate (FWER) correction**\\\n", "Applying FWER correction" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Correct for FWER\n", "pval_FWER = statistics.pval_FWER_correction(result_univariate)\n", "\n", "# Correct p-values using FWER\n", "graphics.plot_p_value_matrix(pval_FWER.T, \n", " title_text =\"Univaraite - FWER\",figsize=(8, 5), \n", " xlabel=\"Time points\", \n", " ylabel=\"HMM States\", \n", " annot=False, \n", " num_x_ticks=11)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Conclusion - Univariate\n", "Using synthetic data, the univariate test compared individual states to reaction time in a pairwise manner. After correcting p-values with the Benjamini/Hochberg method, varying degrees of significance were observed for states 1, 2, 3, 5, and 6 during the same timespan as the multivariate test (27 to 230 timepoints), while state 4 showed minimal or no significance.\n", "\n", "When Family-Wise Error Rate (FWER) correction using the MaxT method was applied separately to the uncorrected data, it removed many of the initially significant points. This indicates that some of the findings from the Benjamini/Hochberg-corrected results may still include false positives, emphasizing the importance of choosing the appropriate correction method depending on the analysis context." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Across-trials within session testing - Multivariate \n", "From our previous result, it is evident that there are variations at specific time windows across multiple sessions. This indicates significant changes occurring during each experimental session in which the subject is involved.\n", "\n", "An intriguing aspect of this dataset is the opportunity to delve into trial-by-trial variability within each experimental session. Even though we observe a significant difference across sessions, it's possible that there are specific periods across trials that shows variability. This hypothesis can be tested using the function ```test_across_trials_within_session```.\n", "\n", "To run the ```test_test_across_trials_within_session``` test, we set the following parameters:\n", "\n", "**Inputs**:\n", "\n", "* ```D_data```: The state time courses of the brain data (```gamma_epoch```).\n", "* ```R_data```: The behavioral measurements (```R_trials```).\n", "* ```idx_data``` : Indices for the first and last trial for a given session (```idx_trials_session```)\n", "\n", "**Settings**:\n", "\n", "* ```method = \"multivariate\"```: Specifies that the test should perform multivariate analysis.\n", "* ```Nnull_samples```: Number of permutations (optional, default is 1000).\n", "\n", "Additional settings allow for the inclusion of confounding variables, which can be regressed out during the permutation testing process. For details on these settings, refer to the function documentation.\n" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "performing permutation testing per timepoint\n", "Total possible permutations: 3.70e+2627\n", "Running number of permutations: 1000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 250/250 [01:46<00:00, 2.35it/s]\n" ] } ], "source": [ "# Set the parameters for across trials testing\n", "method = \"multivariate\"\n", "Nnull_samples = 1000 # Number of permutations (default = 1000)\n", "\n", "# Perform across-trial testing\n", "result_multivaraite_trials =statistics.test_across_trials(Gamma_epoch, \n", " R_sessions, \n", " idx_sessions,\n", " method=method,\n", " Nnull_samples=Nnull_samples)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Visualization of results**\\\n", "Now that we have performed our test, we can then visualize the p-value array.\\\n", "We will use the function ```plot_p_values_over_time``` from the ```graphics``` module.\n", "\n", "Note: Elements marked in red indicate a p-value below 0.05, signifying statistical significance." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot p-values\n", "graphics.plot_p_values_over_time(result_multivaraite_trials[\"pval\"], \n", " title_text =\"Multivariate - Uncorrected\", \n", " xlabel=\"Time points\", \n", " num_x_ticks=11)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Multiple correction\n", "alpha = 0.05\n", "pval_corrected, rejected_corrected =statistics.pval_correction(result_multivaraite_trials, \n", " method='fdr_bh',alpha=alpha)\n", "# Plot p-values\n", "graphics.plot_p_values_over_time(pval_corrected, \n", " title_text =\"Multivariate - Benjamini/Hochberg\",\n", " xlabel=\"HMM States\", \n", " num_x_ticks=11)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Cluster based permutation testing**\\\n", "In order to provide a more strict control over Type I mistakes, we can also apply cluster-based permutation testing to control the Family-Wise Error Rate when conducting multiple comparisons.\n" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pval_cluster =statistics.pval_cluster_based_correction(result_multivaraite_trials)\n", "# Plot p-values\n", "graphics.plot_p_values_over_time(pval_cluster, \n", " title_text =\"Multivariate - Cluster correction\",\n", " xlabel=\"Time points\", \n", " num_x_ticks=11)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Conclusion - Multivariate trials\n", "In the multivariate test across trials, we examined the relationship between state time courses ($D$) and reaction time ($R$) across different trials within experimental sessions.\n", "\n", "After correcting the p-values, no significant differences were found across the time points. This suggests a stable relationship between state time courses and reaction time throughout trials within each session. This stability indicates that, within a single session, the subject's performance remains relatively consistent in terms of the observed state time courses and corresponding reaction times.\n", "\n", "This finding is consistent with how the synthetic data was generated, where reaction times were designed to remain stable across trials within a session but to vary across sessions. While this tutorial primarily focuses on variability between sessions, incorporating trial-to-trial variability within a session provides a more comprehensive view of the data, offering finer insights into performance dynamics." ] } ], "metadata": { "kernelspec": { "display_name": "GLHMM_env", "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": "3.9.21" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }