{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Group Analysis\n",
    "*Written by Luke Chang*\n",
    "\n",
    "In fMRI analysis, we are primarily interested in making inferences about how the brain processes information that is fundamentally similar across all brains even for people that did not directly participate in our study. This requires making inferences about the magnitude of the population level brain response based on measurements from a few randomly sampled participants who were scanned during our experiment.\n",
    "\n",
    "In this tutorial, we will cover how we go from modeling brain responses in each voxel for a single participant to making inferences about the group. We will cover the following topics:\n",
    "\n",
    "- Mixed Effects Models\n",
    "- How to use the summary statistic approach to make inferences at second level\n",
    "- How to perform many types of inferences at second level with different types of design matrics\n",
    "\n",
    "Let's start by watching an overview of group statistics by Tor Wager."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-03T22:53:35.163995Z",
     "start_time": "2020-05-03T22:53:34.988695Z"
    }
   },
   "outputs": [
    {
     "data": {
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jREz0jRE3FOjzGxFQGRNxTVyLoWi6NC6LFYlitJAAQRIABBIEEhUEkAZRIJgCIqQABRICNRq6GmjerUUfj1OTqdvOzWnp2/3SOfXcn1NdxtJXbsjz+09p1KspU6Tcaa6c5GWOr1GplKVWtJpLkZ692nY4d+TfUS1zNNDPaTm/Tkba8GmxGmWGsX8o6Wqp3WSOFvtqT/Fio6qVJ8HxHratWMrp8DJOmmJdL5GQnVjqS2tUqRs3/HEyyqyrSu2Z400aaUfMrDJF23626Olxv1NWk11TS7VlBv8A0mknH/5I08cUrmGpUVTW1J+iajczzf8ALWuvXL2id0muTJOTsvXcFQqu6/bI6yZ6+bqSpJIJCglEAQSAAQSuRJVMsZUIkgCKCrJIZKijKyLSKMiKMTMcxMghEzPVNEzNU9QzWWr6mSqa6nIyVSsESFSGyFSKhMRkRcRkTqh1PmaqZkhzNdM6RWimaIGekaIG4p8RkRUeY2JtTIl0LiXRoMRYoiyKqwEEhQSQSECJIAgAJIIqSs5xhG85KK6tla1WFCk6k3ZI83rtZU1VV5PGMeUfQ5d9zlLcdnU7X09FWg95LpHkcnVbW1NdWT3cekTFLhG9hlDT1K7/ANOEmlxbSbsebry9VnaTKTd3JttlZehr1tClRmo0pTkrcXJevqZJc1Y5o10Xu6Cs+L9BFV3dhOq1a0lCLteVuCuc57WrtNqlD/JcNdCcHCqpG+M1Ugnc42n2hKteNaKT9GlY6dGDUc48YmOo6cX+FKlKzfARKB1XSzjx4iKmjf7USVu8sUaY+jTcXdmijo2n5hlRKmuBLVnKtbU7nTtr9T4IToaUdx5/1SlfmZ5Xq1kn2NemcE1HOOXrHJFkxjrrTZRx/SndM0aTatXSSdOadSHOzfFAqE6y8lOT/gyVtNqI1pN0Kij1xfE1OsZ9vT6TWUdXDKlLj6p80aTxtOpOjVU4SlFrodzQ7YhNRhqHafLL0O07361OnWAhO6unwJK0CSCQAlEAjIsQBFyKCGDIZkVZRlmUZEUfNi5l5Cpv0CFTZmqGibM1RlZrLUMlTmaqnIy1OYYJkKkMkLkVCUMiKiNidUNhzNUOdzJE1QZuK00zRAz0x8TpFPiNixMRkTanIuhSLxNBiZZC0WQUwCqZYqpJKgBYCLgRFiAObtjWbmjuoPzzXZGeupzNowbW1v5FXCL/ANOHL5Zz1PzSTfB8iJPh/YpB+dfweDq/q7WF01Zne2HXjPSVKEoryyvaK4yv1OVoNDV1knZONK/Gb5HejU0uz6apQ4L1fq/5OPk6/iO3i4tu34XqdmR1PHLdv0VrnK12y3paaqb1Ts+KtY7ctdp6dKVWVWOK+TFOjPatDfZOmv8Ay4v1/k589Wffjt34+c9T28zqNLGs1Ju9vQTLR+RpWOjOLjJxkrNcGhbSPTrxY50aDhO7R2dk6mlaVGtNJvjHIyOHDiZdRSeSmlwX6l8Cz9TF5v5uvVwoqL+B6pRkjj7F1rhKOl1F0pf8uTPQKKXoeXvebj3eOzubGR0km3Yx66VDTU8q0lFtcF6s36ytDSUZVJWv+1dWeQ1lSVau5zd5S5m/Hz+vbHl7nHqDU7QjF/8AD0vPb9UuP+A/p/cvaVWvrae+lGGSUldZX6FIUL8WdHZNOFDU5qDnUf6UduvUebj31HptHWrVFeVDdwfXg3/Y1Sk0r24GOT1lOKqyjGol+qEXxRqoV6deiqlN3TPHZ/L3xn1mghqoOUVGNW3CXX+TgzjKEnCosZR4NM9Pxg72vHp0M+v0i1VO9O28XJ9fg6cd56rl5fHvuOTpNfqNJwUsoL9suR3dFtGlq/KvLUtdxZ5maWbU/La6t8lYVHSaqRbi8uFmemdWPNLY9mBz9nbRWoSp1WlU/wCp0DpLrpLqQIIuBYgi4XJRJVsGVZkQykmWbFyZBWQqYxsTJkQqoZqj4D6jM1RlYrPVMs+ZoqczNPmVkqQqQyQuQQmIyIqIyJ1Q2Jph6GWJpg+BuK1QY+BmpsfFnSK0RY2LERY1M2pyZZMWmXTKGJlkxaZZMovclMpcm4Uy4XKXC4F7k3KXC4EzmqcJTk7KKuzyuq1D1FadV+r4fwdnbWow0qpp2dR/4PPX426nl83W3Gequ2ny4Kx0dkbOVear11/pw5J/u/8AozbOoxrajKp/y4LiurNWr2jUpSkqa8voePu35HTx8z/atO09orTpU6dkrckcDVa+pWdru3RDI0a+um5Pixj2fKjHKcbLqTmTlvq9dM2jpOrUiqrtDnY7L18tM4wptS9FE413fym3RUlGe+rvlyHU36c9WfEaqM415uorSk7sRfhw5mjV197NX5R5OxnfH4Nz449fQvW6vcj/AKk8vUrJ+qVzTJUqUpVZVMm3H1PU7M1S1WjjOUllHhPj/k87FxqS8tk+TVys606EalGlUsqn6jHfP6mOvj8n4utW1tctTXeDvShwj8/Jz408nlIilTeeTbZpivixqTJkc+ur1dqsYWskr/Jpo6qOld0vN1EpJHS2Y9JKWFalCc37lcz1fTfjm9OrodbDU0k01l0LSoOFR1tOkpP9cPSXz/Jlr7MVG9fQeSXN0lyf8dDRs/WR1NLimprg01Zpnnv9x7Jb8rTGTau1b4JT6AznT17jqNykrp8TMmt7J9Z9sRp7+EopZv8AV8nNq2goxfPnY27TlGVW7OdU/Wk+h6ePjweT/amRqOju3Tk1K97nq9HqFqNNCp6tcV0Z47L/AFUr8lY7Ww9TjVnRb/VxX8nXmpzfbvXIK5BkbdFrhcpkDZETchsi5DZFDYuTJkyjZERJiZMvJipMiFVGZqjHTZnqMM1nqPiZ5Dqj4CJFZpchUhkhcghMRkRURiOqGRH05eghDKb4morXTZogzLFj4y4HSDTF8RkWIixkZGpVPTLpiVIumaU1MtcUpFlIoYnYtcUmTcBiZNxeROQF7k3F3IyIrh7YrZ63H0grHOnFq6QzUyc6tST5uTCit7KKfNP/AAeHq7dY+1t06lRopS/VJXYqvXjbFxvYbq53grPkcypL0ucZ79u19enX2dtOlp4veRsuonV6ye0qyjTTwXJHJm3K0b2SN2ztfS0N24ZzfK/oLz/LU72fmte4WlgnNLL5F0F+RVcpO1OP+Qp6ha3UXqvg+ZfWThCG6pNJEW59Ir1FVrSlbhyQr0tbiRfgQn8nR57dqWQ7dCPgpNvkvXmVFZzW8iox81+ZfdVZyznwhFWj8hTpqLTtdv1HuSUeDsQUjFIvdJ2VmhUpWZLkrc+LAb6cEXjaMoyg7SXIRTl5XdJr5L3ptckv4YV39m656i6k7SXodBRg5ZYrLqeRzdN50pSUl1ZdbX1K4RnxOPXj9+nq483r27u0tRW02Lp2afU506ueq3jtfqZ46qrXx308rDnDpxE5w66/SurlvJRkZarWa9OBeut20r8zHWk74x5vgdeZ6ebu+0wleUpLhxsjZpqu4qRmucTFTSvw5JD1w9eJpl7CM1OKkuTVybmHZtZz0NPquBqyOjrplyMijkRkRV8iHIpkQ5EFmykmVcijkETJiZyJlITJkS1WbM9RjJyM85BkqoxMi83xFSDKkhci8hcghMRiFIvE6hsWMixSZdFg0wfAdCRkjKw+LTNxWqMhsZGWMxsZGtVoUhikZlIupGtGhSJUhCkWyLoepE5CMicgp2QZCVInIaHZCtTV3enqSvZqPAjIwbUq3hCmut2Y66yajm343t/Jq01NQi5v9y4fwZru3wPrVFuVCL4o8PTXE/lnrVXKbS5CKjjF3ZN2m2mjLUk3Jt8WItqXNv4KMi9y8KTlxtw+Ssr0JVHJYya+TYrt35vqxcI4qxZsJavzIcn0RW9lxYf5KiW+IJWVyv8AIOf/AEIJcnfgRf0SX9il7u9yMr8ih100rlOb9SqdgcunMCyvHhz+SynK3GP+SnNceZCdndAOUsk1a1xTgnLgS7Lj1KZ2kStQ6nNwkdDTVM7cePJnMTUmjZpLxqrozFdOTNpeRRn14HOybeT5yOptOm5aGTXo0zkU1L04/P0a4+MeSZTlPHyx4y9fgvBvrfqysKSS4/8A6XtZcVZG3N3tkT/4S3STNuZy9lz/AOGf/qNmZqOkvpocyMxOZDnxC6dmQ5CcyrmDTnMW5FHPiLcyIvKQqUiJSFSmRBOQiciZSFSYRVsXJlmyjZUUkLkXkxUmEJTGRYlMYmdA1MumKTLpgNTLxlYSmWTNDUpoZGZjUhkZ2NarWpl1J9TIqiLqoXRqU2WzMqqE7xDRqzJz+TLvETvC6rSp/JORmzuG8Jo05h+FHVeabcZW4WEQlnNROhSlZroeX/yPJf8AWO3j5l91x9doZaOcLzUoy/sznVXJu0TftPUOvqZXd4x4I58pWRym/wAs9ZL6Kl5Y2vxKJJlZviTCLZtk2MIrixiZSVopIi9uBYx19NuTlfgJytyZKl1CG34Blf6F5WfEG+gVdy/sVb6lXLiRYCX/AHAhsi4RZMlMWnYnLiAxyKyb5kZEN9Shil/2yjWTK3foWg/MiLDafA6mndopxjxOdTcVLzHSo6ylDy2Xwc+nbk2tNTpyptcJrocuPl8r9OB0d7k3a1jDqYqFd9JK44/o8nv2E4ri7t9CLNu8iqcfktf/AGnRxdDZ8rUWvk17w5ukqWUlyNG8NxWnMMzNvA3gVocyrmIc/krvANDmUcxDqFXUIhspi5TFuVyrYFmyjZDZVsIGxbZLZRsIiTFSZaTFyYCUy6YlMYmbDUy6YpMsmUNTLJikyyYDUybi0yci6G3JUhWRNwpqmy28E3C5dD94G8EXJyGofvAzEZEx80kiarqaGF7z6jNbV3FHnZvghMdVChBL16IyaqpOu85cF6LoeK711terZzzkZJvqZqjNEzNN8TpHClpNsfBJCY3uMV1Fvoiopvc61RekeBVyM9Jyp1ZxmrN8S8maYMjJ9i+Qi/qWUgHX9Cbr0E5F4sC+XQm91a4vNL0Ji7gMXFENAv4BtEFWRyJkUKL3XMq5FW+JVt+lgL5di0JeZCW30CMmBvmiIPjxZbmkLfBmHR0NNO8cWGtheMZpfpdhFCXmRsi23wXDqY+XXT7MZ6WnrT/5dKpL+IsetBq7X/Hml88D0ez6290kHazSsxlaaUWZvlv9NzwTN15Z0qlGXnjjf5DI17TfJ/Jz7nfi7Nefufm4bkGQq4XNsm5EZC8iMiBmRDkUuRcC9yrZVyIbAs5FWyrZVyCJbKSZDkUbAJMW2S2UbASmXTOf+VPpEn8up0ia1HRTLpnM/NqdI9ifzqvth2Y0dRMlM5X59X2w7MnxCt7YdmNV1kybnI8Rre2HZ/ZPiNb2w7P7Lo69ybnH8Sre2HZ/YeJVvbT7P7GjsZE3OP4nW9tPs/sPE63tp9n9jR2MgyOP4nX9tPs/sPE6/tp9n9jR2chlK9+HM4Xidf20+z+xkNs6iCsoUuz+zPV2ZF5uV6CEFF5S4v5K1pp8jhPbeqf7aXZ/ZR7X1D/bT7P7OP5rp+46tWRm5swS2lWlzjDs/sqtfVvfGHZmsYvUdOKHQXU5HiNb2w7P7J8Urpfpp9n9lk9mw/U8dc7crK4uc7vhyMr1NRylKyvJ3ZXfS6I1WWu/Qm5k/In0iT+TPpEg2XGQZg/Kn0iT+ZU6R7BHRfwy0X6HN/Nqe2HZk/n1fbDswOjOXDgyinZ2ZgetqP8AbDsR+ZU9sOwV0nJMo2v7GD8up0iR+XU6R7BG5yKN8eBj/Jn0iH5M+kQrXk+pMZceZj/Jn0iH5M+kQO7B3pxfwVkcqG060IqKjCy6p/ZL2nWf7afZ/ZMa/UdeizpUOMUzy0dp1o8o0+z+x0NuaqHKNLs/szea3O5Hstm6jdznTfrxRsq1bp3PCf8AiDVqakoUU18P7Gv+qNc+dOh/7X9nO+KuvPm5kx6LX+am/g5eRzZ/1Fq5xadOhx/2v7M3itf20+z+zrxLJlcfJ1OrsdvILnE8Vr+2n2f2Hitf20+z+zbnrt5BkcTxWv7afZ/YeK1/bT7P7BrtZBkcXxWv7afZ/YeKV/bT7P7A7LkVcjj+KV/bT7P7DxOt7afZ/YR1nIq5HK8Sre2HZ/ZHiNb2w7P7A6jZVs5viFX2w7Mj8+r7YdmB0GyjZhetqv0j2I/LqdIgZwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA//Z",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/__cOYPifDWk\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7f98f039cad0>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "\n",
    "YouTubeVideo('__cOYPifDWk')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hierarchical Data Structure\n",
    "We can think of the data as being organized into a hierarchical structure. For each brain, we are measuring BOLD activity in hundreds of thousands of cubic voxels sampled at about 0.5Hz (i.e., TR=2s). Our experimental task will have many different trials for each condition (seconds), and these trials may be spread across multiple scanning runs (minutes), or entire scanning sessions (hours). We are ultimately interested in modeling all of these different scales of data to make an inference about the function of a particular region of the brain across the group of participants we sampled, which we would hope will generalize to the broader population.\n",
    "\n",
    "![HierarchicalStructure.png](../images/group_analysis/HierarchicalStructure.png)\n",
    "\n",
    "In the past few notebooks, we have explored how to preprocess the data to reduce noise and enhance our signal and also how we can estimate responses in each voxel to specific conditions within a single participant based on convolving our experimental design with a canonical hemodynamic response function (HRF). Here we will discuss how we combine these brain responses estimated at the first-level in a second-level model to make inferences about the group."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling Mixed Effects\n",
    "Let's dive deeper into how we can model both random and fixed effects using multi-level models by watching another video by Tor Wager."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-30T17:57:02.383803Z",
     "start_time": "2020-04-30T17:57:02.211461Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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       "            src=\"https://www.youtube.com/embed/-abMLQSjMSI\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7fc8f8579590>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo('-abMLQSjMSI')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the statistics we have discussed to this point have assumed that the data we are trying to model are drawn from an identical distribution and that they are independent of each other. For example, each group of participants that complete each version of our experiment are assumed to be random sample of the larger population. However, if there was some type of systematic bias in our sampling strategy, our group level statistics would not necessarily reflect a random draw from the population-level Gaussian distribution. However, as should already be clear from the graphical depiction of the hierarchical structure of our data above, our data are not always independent. For example, we briefly discussed this in the GLM notebook, but voxel responses within the same participant are not necessarily independent as there appears to be a small amount of autocorrelation in the BOLD response. This requires whitening the data to meet the independence assumption. What is clear from the hierarchy is that all of the data measured from one participant are likely to be more similar to each other than another participant. In fact, it is almost always the case that the variance *within* a subject $\\sigma_{within}^2$ is almost always smaller than the variance *across* participants $\\sigma_{between}^2$. If we combined all of the data from all participants and treated them as if they were independent, we would likely have an inflated view of the group effect (this was historically referred to as a \"fixed effects group analysis\").\n",
    "\n",
    "This problem has been elegantly solved in statistics in a class of models called *mixed effects models*. Mixed effects models are an extension of regression that allows data to be structured into groups and coefficients to vary by groups. They are referred to differently in different scientific domains, for example they may be referred to as multilevel, hierarchical, or panel models. The reason that this framework has been found to be useful in many different fields, is that it is particularly well suited for modeling clustered data, such as students in a classroom and also longitudinal or repeated data, such as within-subject designs. \n",
    "\n",
    "The term \"mixed\" comes from the fact that these models are composed of both *fixed* and *random* effects. Fixed effects refer to parameters describing the amount of variance that a feature explains of an outcome variable. Fixed factors are often explicitly manipulated in an experiment and can be categorical (e.g., gender) or continuous (e.g., age). We assume that the magnitude of these effects are *fixed* in the population, but that the observed signal strength will vary across sessions and subjects. This variation can be decomposed into different sources of variance, such as: \n",
    "    - Measurement or Irreducible Error\n",
    "    - Response magnitude that varies randomly across subjects.\n",
    "    - Response magnitude that varies randomly across different elicitations (e.g., trials or sessions).\n",
    "\n",
    "Modeling these different sources of variance allows us to have a better idea of how generalizable our estimates might be to another participant or trial.\n",
    "\n",
    "As an example, imagine if we were interested if there were any gender differences between the length of how males and females cut their hair. We might sample a given individual several times over the course of a couple of years to get an accurate measurement of how long they keep their hair. These samples are akin to trials and will give us a way to represent the overall tendency of the length an individual keeps their hair in the form of a distribution. Narrow distributions mean that there is little variability in the length of the hair at each measurement, while wider distributions indicate more variation in the hair length across time. Of course, we are most interested not in the length of how an individual cuts their hair, but rather how many individuals from the same group cut their hair. This requires measuring multiple participants, who will all vary randomly around some population level hair length parameter. We are interested in modeling the true *fixed effect* of what the population parameter is for hair length, and specifically, whether this differs across gender. The variation in measurements within an individual and across individuals will reflect some degree of randomness that we need to account for in order to estimate a parameter that will generalize beyond the participants we measured their hair, but to new participants. \n",
    "\n",
    "![MixedEffects.png](../images/group_analysis/MixedEffects.png)\n",
    "from Poldrack, Mumford, & Nichols (2011)\n",
    "\n",
    "In statistics, it is useful to distinguish between the *model* used to describe the data, the *method* of parameter estimation, and the *algorithm* used to obtain them. \n",
    "\n",
    "Let's now watch a video by Martin Lindquist to learn more about the way these models are estimated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-30T18:09:39.617940Z",
     "start_time": "2020-04-30T18:09:39.452877Z"
    }
   },
   "outputs": [
    {
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",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/-yaHTygR9b8\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7fc8f8582a10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo('-yaHTygR9b8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First Level - Single Subject Model\n",
    "\n",
    "In fMRI data analysis, we often break analyses into multiple stages. First, we are interested in estimating the parameter (or distribution) of signal in a given region resulting from our experimental manipulation, while simultaneously attempting to control for as much noise and artifacts as possible. This will give us a a single number for each participant of the average length they keep their hair.\n",
    "\n",
    "At the first level model, for each participant we can define our model as:\n",
    "\n",
    "$Y_i = X_i\\beta + \\epsilon_i$, where $i$ is an observation for a single participant and $\\epsilon_i \\sim \\mathcal{N}(0, \\sigma_i^2)$\n",
    "\n",
    "Because participants are independent, it is possible to estimate each participant separately.\n",
    "\n",
    "To provide a concrete illustration of the different sources of variability in a signal, let's make a quick simulation a hypothetical voxel timeseries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-14T22:53:07.935259Z",
     "start_time": "2020-10-14T22:53:07.446235Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from nltools.stats import regress, zscore\n",
    "from nltools.data import Brain_Data, Design_Matrix\n",
    "from nltools.stats import regress \n",
    "from nltools.external import glover_hrf\n",
    "from scipy.stats import ttest_1samp\n",
    "\n",
    "def plot_timeseries(data, linewidth=3, labels=None, axes=True):\n",
    "    f,a = plt.subplots(figsize=(20,5))\n",
    "    a.plot(data, linewidth=linewidth)\n",
    "    a.set_ylabel('Intensity', fontsize=18)\n",
    "    a.set_xlabel('Time', fontsize=18)\n",
    "    plt.tight_layout()\n",
    "    if labels is not None:\n",
    "        plt.legend(labels, fontsize=18)\n",
    "    if not axes:\n",
    "        a.axes.get_xaxis().set_visible(False)\n",
    "        a.axes.get_yaxis().set_visible(False)\n",
    "        \n",
    "def simulate_timeseries(n_tr=200, n_trial=5, amplitude=1, tr=1, sigma=0.05):\n",
    "    y = np.zeros(n_tr)\n",
    "    y[np.arange(20, n_tr, int(n_tr/n_trial))] = amplitude\n",
    "\n",
    "    hrf = glover_hrf(tr, oversampling=1)\n",
    "    y = np.convolve(y, hrf, mode='same')\n",
    "    epsilon = sigma*np.random.randn(n_tr)\n",
    "    y = y + epsilon\n",
    "    return y\n",
    "\n",
    "sim1 = simulate_timeseries(sigma=0)\n",
    "sim2 = simulate_timeseries(sigma=0.05)\n",
    "plot_timeseries(np.vstack([sim1,sim2]).T, labels=['Signal', 'Noisy Signal'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the noise appears to be independent over each TR."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Second level summary of between group variance\n",
    "\n",
    "In the second level model, we are interested in relating the subject specific parameters contained in $\\beta$ to the population parameters $\\beta_g$.  We assume that the first level parameters are randomly sampled from a population of possible regression parameters.\n",
    "\n",
    "$\\beta = X_g\\beta_g + \\eta$\n",
    "\n",
    "$\\eta \\sim \\mathcal{N}(0,\\,\\sigma_g^{2})$ \n",
    "\n",
    "Now let's add noise onto the beta parameter to see what happens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-03T22:53:51.453599Z",
     "start_time": "2020-05-03T22:53:51.208662Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "beta = np.abs(np.random.randn())*3\n",
    "sim1 = simulate_timeseries(sigma=0)\n",
    "sim2 = simulate_timeseries(sigma=0.05)\n",
    "sim3 = simulate_timeseries(amplitude=beta, sigma=0.05)\n",
    "plot_timeseries(np.vstack([sim1,sim2,sim3]).T, labels=['Signal', 'Noisy Signal', 'Noisy Beta + Noisy Signal'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try running the above code several times. Can you see how the beta parameter impacts the amplitude of each trial, while the noise appears to be random and uncorrelated with the signal?\n",
    "\n",
    "Let's try simulating three subjects with a beta drawn from a normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-03T22:53:54.110090Z",
     "start_time": "2020-05-03T22:53:53.863080Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim1 = simulate_timeseries(amplitude=np.abs(np.random.randn())*2, sigma=0.05)\n",
    "sim2 = simulate_timeseries(amplitude=np.abs(np.random.randn())*2, sigma=0.05)\n",
    "sim3 = simulate_timeseries(amplitude=np.abs(np.random.randn())*2, sigma=0.05)\n",
    "plot_timeseries(np.vstack([sim1, sim2, sim3]).T, labels=['Subject 1', 'Subject 2', 'Subject 3'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make an inference if there is a reliable difference within or across groups, we need to model the distribution of the parameters resulting from the first level model using a second level model. For example, if we were solely interested in estimating the average length men keep their hair, we would need to measure hair lengths from lots of different men and the average would be our best guess for any new male sampled from the same population. In our example, we are explicitly interested in the pairwise difference between males and females in hair length. Does the mean hair length for one sex significantly different from the hair length of the other group that is larger than the variations in hair length we observe within each group?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mixed Effects Model\n",
    "\n",
    "In neuroimaging data analysis, there are two main approaches to implementing these different models. Some software packages attempt to use a computationally efficient approximation and use what is called a two stage summary statistic approach. First level models are estimated separately for every participant and then the betas from each participant's model is combined in a second level model. This is the strategy implemented in SPM and is computationally efficient. However, another approach simultaneously estimates the first and second level models at the same time and often use algorithms that iterate back and forth from the single to the group. The main advantage of this approach over the two-stage approach is that the uncertainty in the parameter estimates at the first-level can be appropriately weighted at the group level. For example, if we had a bad participant with very noisy data, we might not want to weight their estimate when we aggregate everyone's data across the group. The disadvantage of this approach is that the estimation procedure is considerably more computationally expensive. This is the approach implemented in FSL, BrainVoyager, and AFNI. In practice, the advantage of the true random effects simultaneous parameter estimation only probably benefits getting more reliable estimates when the sample size is small. In the limit, both methods should converge to the same answer. For a more in depth comparison see this [blog post](http://eshinjolly.com/2019/02/18/rep_measures/) by Eshin Jolly.\n",
    "\n",
    "A full mixed effects model can be written as, \n",
    "\n",
    "$$Y_i = X_i(X_g\\beta_g + \\eta) +\\epsilon_i$$\n",
    "\n",
    "         or\n",
    "\n",
    "$$Y \\sim \\mathcal(XX_g\\beta_g, X\\sigma_g^2X^T + \\sigma^2)$$\n",
    "\n",
    "![TwoLevelModel.png](../images/group_analysis/TwoLevelModel.png)\n",
    "\n",
    "from Poldrack, Mumford, & Nichols (2011)\n",
    "\n",
    "Let's now try to recover the beta estimates from our 3 simulated subjects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-03T22:54:01.052864Z",
     "start_time": "2020-05-03T22:54:00.929577Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Estimated Beta')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a design matrix with an intercept and predicted response\n",
    "task = simulate_timeseries(amplitude=1, sigma=0)\n",
    "X = np.vstack([np.ones(len(task)), task]).T\n",
    "\n",
    "# Loop over each of the simulated participants and estimate the amplitude of the response.\n",
    "betas = []\n",
    "for sub in [sim1, sim2, sim3]:\n",
    "    beta,_,_,_,_,_ = regress(X, sub)\n",
    "    betas.append(beta[1])\n",
    "\n",
    "# Plot estimated amplitudes for each participant\n",
    "plt.bar(['Subject1', 'Subject2', 'Subject3'], betas)\n",
    "plt.ylabel('Estimated Beta', fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T09:48:39.720446Z",
     "start_time": "2019-04-24T09:48:39.716207Z"
    }
   },
   "source": [
    "What if we simulated lots of participants?  What would the distribution of betas look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-03T22:54:04.394212Z",
     "start_time": "2020-05-03T22:54:04.100063Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f990c25ced0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a design matrix with an intercept and predicted response\n",
    "task = simulate_timeseries(amplitude=1, sigma=0)\n",
    "X = np.vstack([np.ones(len(task)), task]).T\n",
    "\n",
    "# Loop over each of the simulated participants and estimate the amplitude of the response.\n",
    "betas = []\n",
    "for sub in range(100):\n",
    "    sim = simulate_timeseries(amplitude=2+np.random.randn()*2, sigma=0.05)\n",
    "    beta,_,_,_,_,_ = regress(X, sim)\n",
    "    betas.append(beta[1])\n",
    "\n",
    "# Plot distribution of estimated amplitudes for each participant\n",
    "plt.hist(betas)\n",
    "plt.ylabel('Frequency', fontsize=18)\n",
    "plt.xlabel('Estimated Beta', fontsize=18)\n",
    "plt.axvline(x=0, color='r', linestyle='dashed', linewidth=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now in a second level analysis, we are interested in whether there is a reliable effect across all participants in our sample. In other words, is there a response to our experiment for a specific voxel that is reliably present across our sample of participants?\n",
    "\n",
    "We can test this hypothesis in our simulation by running a one-sample ttest across the estimated first-level betas at the second level. This allows us to test whether the sample has signal that is reliably different from zero (i.e., the null hypothesis)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-03T22:54:06.748859Z",
     "start_time": "2020-05-03T22:54:06.744249Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_1sampResult(statistic=10.854776909716737, pvalue=1.50775361636617e-18)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_1samp(betas, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T10:01:29.469805Z",
     "start_time": "2019-04-24T10:01:29.466100Z"
    }
   },
   "source": [
    "What did we find?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(content:group_analysis:labels)=\n",
    "## Running a Group Analysis\n",
    "\n",
    "Okay, now let's try and run our own group level analysis with real imaging data using the Pinel Localizer data. I have run a first level model for the first 10 participants using the procedure we used in the single-subject analysis notebook. \n",
    "\n",
    "Here is the code I used to complete this for all participants. I wrote all of the betas and also a separate file for each individual regressor of interest.\n",
    "\n",
    "```\n",
    "import os\n",
    "from glob import glob\n",
    "from tqdm import tqdm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import nibabel as nib\n",
    "from nltools.stats import zscore, regress, find_spikes\n",
    "from nltools.data import Brain_Data, Design_Matrix\n",
    "from bids import BIDSLayout, BIDSValidator\n",
    "from nltools.file_reader import onsets_to_dm\n",
    "from nltools.data import Brain_Data, Design_Matrix\n",
    "from nilearn.plotting import view_img, glass_brain, plot_stat_map\n",
    "\n",
    "data_dir = '../data/localizer'\n",
    "layout = BIDSLayout(data_dir, derivatives=True)\n",
    "\n",
    "tr = layout.get_tr()\n",
    "fwhm = 6\n",
    "spike_cutoff = 3\n",
    "\n",
    "def load_bids_events(layout, subject):\n",
    "    '''Create a design_matrix instance from BIDS event file'''\n",
    "    \n",
    "    tr = layout.get_RepetitionTime()[0]\n",
    "    n_tr = nib.load(layout.get(subject=subject, scope='derivatives', suffix='bold', return_type='filename', extension='nii.gz')[0]).shape[-1]\n",
    "\n",
    "    onsets = pd.read_csv(layout.get(subject=subject, suffix='events')[0].path, sep='\\t')\n",
    "    onsets.columns = ['Onset', 'Duration', 'Stim']\n",
    "    return onsets_to_dm(onsets, sampling_freq=1/tr, run_length=n_tr)\n",
    "\n",
    "def make_motion_covariates(mc):\n",
    "    z_mc = zscore(mc)\n",
    "    all_mc = pd.concat([z_mc, z_mc**2, z_mc.diff(), z_mc.diff()**2], axis=1)\n",
    "    all_mc.fillna(value=0, inplace=True)\n",
    "    return Design_Matrix(all_mc, sampling_freq=1/tr)\n",
    "\n",
    "\n",
    "\n",
    "# Create output folder if it doesn't exist yet\n",
    "if not os.path.exists('../data/localizer/derivatives/betas'):\n",
    "    os.mkdir('../data/localizer/derivatives/betas')\n",
    "        \n",
    "for sub in tqdm(layout.get_subjects(scope='derivatives')):\n",
    "    data = Brain_Data(layout.get(subject=sub, scope='derivatives', suffix='bold', extension='nii.gz', return_type='file')[0])\n",
    "    data = data.smooth(fwhm=fwhm)\n",
    "    dm = load_bids_events(layout, sub)\n",
    "    covariates = pd.read_csv(layout.get(subject=sub, scope='derivatives', extension='.tsv')[0].path, sep='\\t')\n",
    "    mc_cov = make_motion_covariates(covariates[['trans_x','trans_y','trans_z','rot_x', 'rot_y', 'rot_z']])\n",
    "    spikes = data.find_spikes(global_spike_cutoff=spike_cutoff, diff_spike_cutoff=spike_cutoff)\n",
    "    dm_cov = dm.convolve().add_dct_basis(duration=128).add_poly(order=1, include_lower=True)\n",
    "    dm_cov = dm_cov.append(mc_cov, axis=1).append(Design_Matrix(spikes.iloc[:, 1:], sampling_freq=1/tr), axis=1)\n",
    "    data.X = dm_cov\n",
    "    stats = data.regress()\n",
    "\n",
    "    # Write out all betas\n",
    "    stats['beta'].write(f'../data/localizer/derivatives/betas/{sub}_betas.nii.gz')\n",
    "\n",
    "    # Write out separate beta for each condition\n",
    "    for i, name in enumerate([x[:-3] for x in dm_cov.columns[:10]]):\n",
    "        stats['beta'][i].write(f'../data/localizer/derivatives/betas/{sub}_beta_{name}.nii.gz')\n",
    "```\n",
    "\n",
    "Now, we are ready to run our first group analyses! \n",
    "\n",
    "Let's load our design matrix to remind ourselves of the various conditions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-23T03:07:50.337051Z",
     "start_time": "2019-04-23T03:07:50.162181Z"
    }
   },
   "source": [
    "### One Sample t-test\n",
    "\n",
    "For our first group analysis, let's try to examine which regions of the brain are consistently activated across participants. We will just load the first regressor in the design matrix - *horizontal_checkerboard*.\n",
    "\n",
    "We will use the `glob` function to search for all files that contain the name *horizontal_checkerboard* in each subject's folder. We will then sort the list and load all of the files using the `Brain_Data` class.  This will take a little bit to load all of the data into ram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-03T23:44:28.046686Z",
     "start_time": "2020-05-03T23:44:25.672906Z"
    }
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "\n",
    "con1_name = 'horizontal_checkerboard'\n",
    "con1_file_list = glob.glob(os.path.join(data_dir, 'derivatives','fmriprep','*', 'func', f'sub*_{con1_name}*nii.gz'))\n",
    "con1_file_list.sort()\n",
    "con1_dat = Brain_Data(con1_file_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the data loaded, we can run quick operations such as, what is the mean activation in each voxel across participants?  Or, what is the standard deviation of the voxel activity across participants?\n",
    "\n",
    "Notice how we can chain different commands like `.mean()` and `.plot()`.  This makes it easy to quickly manipulate the data similar to how we use tools like pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-03T23:45:36.141324Z",
     "start_time": "2020-05-03T23:45:34.130864Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "threshold is ignored for simple axial plots\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1080x144 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "con1_dat.mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the `ttest()` method to run a quick t-test across each voxel in the brain. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-03T23:45:38.104173Z",
     "start_time": "2020-05-03T23:45:37.999184Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['t', 'p'])\n"
     ]
    }
   ],
   "source": [
    "con1_stats = con1_dat.ttest()\n",
    "\n",
    "print(con1_stats.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This return a dictionary of a map of the t-values and a separate one containing the p-value for each voxel.\n",
    "\n",
    "For now, let's look at the results of the t-ttest and threshold them to something like t>4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-03T23:45:42.577573Z",
     "start_time": "2020-05-03T23:45:40.294505Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df9deeea0d2c4c99b3dadd1c0a0428df",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatText(value=0.0, description='Threshold'), IntSlider(value=0, continuous_update=Fals…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "con1_stats['t'].iplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see we see very clear activation in various parts of visual cortex, which we expected from the visual stimulation.\n",
    "\n",
    "However, if wanted to test the hypothesis that there are specific areas of early visual cortex (e.g., V1) that process edge orientations, we could run a specific contrast comparing vertical orientations with horizontal orientations.  \n",
    "\n",
    "Now we need to load the vertical data and create a contrast between horizontal and vertical checkerboards.\n",
    "\n",
    "Here a contrast is simply [1, -1] and can be achieved by simply subtracting the two images (assuming the subject images are sorted in the same order)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-03T23:46:23.778153Z",
     "start_time": "2020-05-03T23:46:21.446735Z"
    }
   },
   "outputs": [],
   "source": [
    "con2_name = 'vertical_checkerboard'\n",
    "con2_file_list = glob.glob(os.path.join(data_dir, 'derivatives','fmriprep','*', 'func', f'sub*_{con2_name}*nii.gz'))\n",
    "con2_file_list.sort()\n",
    "con2_dat = Brain_Data(con2_file_list)\n",
    "\n",
    "con1_v_con2 = con1_dat-con2_dat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we will now run a one-sample ttest on the contrast to find regions that are consistently different in viewing horizontal vs vertical checkerboards across participants at the group level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-03T23:46:28.152287Z",
     "start_time": "2020-05-03T23:46:26.001818Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b7a76208c164fc9bbe96cd0af599699",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatText(value=0.0, description='Threshold'), HTML(value='Image is 3D', description='Vo…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "con1_v_con2_stats = con1_v_con2.ttest()\n",
    "con1_v_con2_stats['t'].iplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group statistics using design matrices\n",
    "\n",
    "For these analyses we ran a one-sample t-test to examine the average activation to horizontal checkerboards and the difference between viewing horizontal and vertical checkerboards. This is equivalent to a vector of ones at the second level. The latter analysis is technically a paired-samples t-test.\n",
    "\n",
    "Do these tests sound familiar?\n",
    "\n",
    "It turns out that most parametric statistical tests are just special cases of the general linear model.  Here are what the design matrices would look like for various types of statistical tests.\n",
    "\n",
    "\n",
    "![DesignMatrices.png](../images/group_analysis/DesignMatrices.png)\n",
    "from Poldrack, Mumford, & Nichols 2011\n",
    "\n",
    "In this section, we will explore how we can formulate different types of statistical tests using a regression through simulations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One Sample t-test\n",
    "\n",
    "Just to review, our one sample t-test can also be formulated as a regression, where the beta values for each subject in a voxel are predicted by a vector of ones. This *intercept* only model, computes the mean of $y$. If the mean of $y$ (i.e., the intercept) is consistently shifted away from zero, then we can reject the null hypothesis that the mean of the betas is zero.\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "s_1 \\\\\n",
    "s_2 \\\\\n",
    "s_3 \\\\\n",
    "s_4 \\\\\n",
    "s_5 \\\\\n",
    "s_6\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "=\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "1 \\\\\n",
    "1 \\\\\n",
    "1 \\\\\n",
    "1 \\\\\n",
    "1 \\\\\n",
    "1\n",
    "\\end{bmatrix}\n",
    "\\begin{bmatrix}\n",
    "\\beta_0 \n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "We can simulate this by generating data from a Gaussian distribution. We will generate two groups, where $y$ reflects equal draws from each of these distributions ${group_1} = \\mathcal{N}(10, 2)$ and ${group_2} = \\mathcal{N}(5, 2)$. We then regress a vector of ones on $y$.\n",
    "\n",
    "We report the estimated value of beta and compare it to various summaries of the simulated data. This allows us to see exactly what each parameter in the regression is calculating.\n",
    "\n",
    "First, let's define a function `run_regression_simulation` to help us generate plots and calculate various ways to summarize the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-03T23:53:29.180955Z",
     "start_time": "2020-05-03T23:53:29.168940Z"
    }
   },
   "outputs": [],
   "source": [
    "def run_regression_simulation(x, y, paired=False):\n",
    "    '''This Function runs a regression and outputs results'''\n",
    "    # Estimate Regression\n",
    "    if not paired:\n",
    "        b, t, p, df, res = regress(x, y)\n",
    "        print(f\"betas: {b}\")\n",
    "        if x.shape[1] > 1:\n",
    "            print(f\"beta1 + beta2: {b[0] + b[1]}\")\n",
    "            print(f\"beta1 - beta2: {b[0] - b[1]}\")\n",
    "            print(f\"mean(group1): {np.mean(group1)}\")\n",
    "            print(f\"mean(group2): {np.mean(group2)}\")\n",
    "            print(f\"mean(group1) - mean(group2): {np.mean(group1)-np.mean(group2)}\")\n",
    "        print(f\"mean(y): {np.mean(y)}\")\n",
    "    else:\n",
    "        beta, t, p, df, res = regress(x, y)\n",
    "        a = y[x.iloc[:,0]==1]\n",
    "        b = y[x.iloc[:,0]==-1]\n",
    "        out = []\n",
    "        for sub in range(1, X.shape[1]):\n",
    "            sub_dat = y[X.iloc[:, sub]==1]\n",
    "            out.append(sub_dat-np.mean(sub_dat))\n",
    "        avg_sub_mean_diff = np.mean([x[0] for x in out])\n",
    "        print(f\"betas: {b}\")\n",
    "        print(f\"contrast beta: {beta[0]}\")\n",
    "        print(f\"mean(subject betas): {np.mean(beta[1:])}\")\n",
    "        print(f\"mean(y): {np.mean(y)}\")\n",
    "        print(f\"mean(a): {a.mean()}\")\n",
    "        print(f\"mean(b): {b.mean()}\")\n",
    "        print(f\"mean(a-b): {np.mean(a - b)}\")\n",
    "        print(f\"sum(a_i-mean(y_i))/n: {avg_sub_mean_diff}\")\n",
    "\n",
    "    # Create Plot\n",
    "    f,a = plt.subplots(ncols=2, sharey=True)\n",
    "    sns.heatmap(pd.DataFrame(y), ax=a[0], cbar=False, yticklabels=False, xticklabels=False)\n",
    "    sns.heatmap(x, ax=a[1], cbar=False, yticklabels=False)\n",
    "    a[0].set_ylabel('Subject Values', fontsize=18)    \n",
    "    a[0].set_title('Y')    \n",
    "    a[1].set_title('X')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "okay, now let's run the simulation for the one sample t-test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-03T23:53:30.680115Z",
     "start_time": "2020-05-03T23:53:30.556097Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "betas: 7.767465428733612\n",
      "mean(y): 7.767465428733613\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "group1_params = {'n':20, 'mean':10, 'sd':2}\n",
    "group2_params = {'n':20, 'mean':5, 'sd':2}\n",
    "group1 = group1_params['mean'] + np.random.randn(group1_params['n']) * group1_params['sd']\n",
    "group2 = group2_params['mean'] + np.random.randn(group2_params['n']) * group2_params['sd']\n",
    "\n",
    "y = np.hstack([group1, group2])\n",
    "x = pd.DataFrame({'Intercept':np.ones(len(y))})\n",
    "    \n",
    "run_regression_simulation(x, y)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results of this simulation clearly demonstrate that the intercept of the regression is modeling the mean of $y$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Independent-Samples T-Test - Dummy Codes\n",
    "\n",
    "Next, let's explore how we can compute an independent-sample t-test using a regression. There are several different ways to compute this. Each of them provides a different way to test for differences between the means of the two samples.\n",
    "\n",
    "First, we will explore how dummy codes can be used to test for group differences. We will create a design matrix with an intercept and also a column with a binary regressor indicating group membership. The target group will be ones, and the reference group will be zeros.\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "s_1 \\\\\n",
    "s_2 \\\\\n",
    "s_3 \\\\\n",
    "s_4 \\\\\n",
    "s_5 \\\\\n",
    "s_6\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "=\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "1 & 1\\\\\n",
    "1 & 1\\\\\n",
    "1 & 1\\\\\n",
    "1 & 0\\\\\n",
    "1 & 0\\\\\n",
    "1 & 0\n",
    "\\end{bmatrix}\n",
    "\\begin{bmatrix}\n",
    "\\beta_0 \\\\\n",
    "\\beta_1\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "Let's run another simulation examining what the regression coefficients reflect using this dummy code approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-03T23:53:32.116359Z",
     "start_time": "2020-05-03T23:53:31.984376Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "betas: [5.59590636 4.86905406]\n",
      "beta1 + beta2: 10.46496042000535\n",
      "beta1 - beta2: 0.7268523061306578\n",
      "mean(group1): 10.464960420005351\n",
      "mean(group2): 5.595906363068004\n",
      "mean(group1) - mean(group2): 4.869054056937347\n",
      "mean(y): 8.030433391536677\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "group1_params = {'n':20, 'mean':10, 'sd':2}\n",
    "group2_params = {'n':20, 'mean':5, 'sd':2}\n",
    "group1 = group1_params['mean'] + np.random.randn(group1_params['n']) * group1_params['sd']\n",
    "group2 = group2_params['mean'] + np.random.randn(group2_params['n']) * group2_params['sd']\n",
    "\n",
    "y = np.hstack([group1, group2])\n",
    "x = pd.DataFrame({'Intercept':np.ones(len(y)), 'Contrast':np.hstack([np.ones(group1_params['n']), np.zeros(group2_params['n'])])})\n",
    "\n",
    "run_regression_simulation(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can you figure out what the beta estimates are calculating?\n",
    "\n",
    "The intercept $\\beta_0$ is now the mean of the reference group, and the estimate of the dummy code regressor $\\beta_1$ indicates the difference of the mean of the target group from the reference group. \n",
    "\n",
    "Thus, the mean of the reference group is $\\beta_0$ or the intercept, and the mean of the target group is $\\beta_1 + \\beta_2$. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Independent-Samples T-Test - Contrasts\n",
    "\n",
    "Another way to compare two different groups is by creating a model with an intercept and contrast between the two groups.\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "s_1 \\\\\n",
    "s_2 \\\\\n",
    "s_3 \\\\\n",
    "s_4 \\\\\n",
    "s_5 \\\\\n",
    "s_6\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "=\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "1 & 1\\\\\n",
    "1 & 1\\\\\n",
    "1 & 1\\\\\n",
    "1 & -1\\\\\n",
    "1 & -1\\\\\n",
    "1 & -1\n",
    "\\end{bmatrix}\n",
    "\\begin{bmatrix}\n",
    "\\beta_0 \\\\\n",
    "\\beta_1\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "Let's now run another simulation to see how these beta estimates differ from the dummy code model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-03T23:53:33.527554Z",
     "start_time": "2020-05-03T23:53:33.388617Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "betas: [7.60615263 3.16144295]\n",
      "beta1 + beta2: 10.767595577579165\n",
      "beta1 - beta2: 4.444709673800184\n",
      "mean(group1): 10.767595577579163\n",
      "mean(group2): 4.4447096738001814\n",
      "mean(group1) - mean(group2): 6.322885903778982\n",
      "mean(y): 7.606152625689674\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "group1_params = {'n':20, 'mean':10, 'sd':2}\n",
    "group2_params = {'n':20, 'mean':5, 'sd':2}\n",
    "group1 = group1_params['mean'] + np.random.randn(group1_params['n']) * group1_params['sd']\n",
    "group2 = group2_params['mean'] + np.random.randn(group2_params['n']) * group2_params['sd']\n",
    "\n",
    "y = np.hstack([group1, group2])\n",
    "x = pd.DataFrame({'Intercept':np.ones(len(y)), 'Contrast':np.hstack([np.ones(group1_params['n']), -1*np.ones(group2_params['n'])])})\n",
    "\n",
    "run_regression_simulation(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-01T04:54:40.382948Z",
     "start_time": "2019-05-01T04:54:40.373846Z"
    }
   },
   "source": [
    "So, just as before, the intercept reflects the mean of $y$. Now can you figure out what $\\beta_1$ is calculating?\n",
    "\n",
    "It is the average distance of each group to the mean. The mean of group 1 is $\\beta_0 + \\beta_1$ and the mean of group 2 is $\\beta_0 - \\beta_1$.\n",
    "\n",
    "Remember that in our earlier discussion of contrast codes, we noted the importance of balanced codes across regressors. What if the group sizes are unbalanced?  Will this effect our results?\n",
    "\n",
    "To test this, we will double the sample size of group1 and rerun the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-03T23:53:34.679707Z",
     "start_time": "2020-05-03T23:53:34.481854Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "betas: [7.98765299 2.16002722]\n",
      "beta1 + beta2: 10.147680205027868\n",
      "beta1 - beta2: 5.827625771635669\n",
      "mean(group1): 10.147680205027864\n",
      "mean(group2): 5.8276257716356685\n",
      "mean(group1) - mean(group2): 4.320054433392196\n",
      "mean(y): 8.707662060563798\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "group1_params = {'n':40, 'mean':10, 'sd':2}\n",
    "group2_params = {'n':20, 'mean':5, 'sd':2}\n",
    "group1 = group1_params['mean'] + np.random.randn(group1_params['n']) * group1_params['sd']\n",
    "group2 = group2_params['mean'] + np.random.randn(group2_params['n']) * group2_params['sd']\n",
    "\n",
    "y = np.hstack([group1, group2])\n",
    "x = pd.DataFrame({'Intercept':np.ones(len(y)), 'Contrast':np.hstack([np.ones(group1_params['n']), -1*np.ones(group2_params['n'])])})\n",
    "\n",
    "run_regression_simulation(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like the beta estimates are identical to the previous simulation. This demonstrates that we *do not* need to adjust the weights of the number of ones and zeros to sum to zero.  This is because the beta is estimating the average distance from the mean, which is invariant to group sizes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Independent-Samples T-Test - Group Intercepts\n",
    "\n",
    "The third way to calculate an independent samples t-test using a regression is to split the intercept into two separate binary regressors with each reflecting the membership of each group. There is no need to include an intercept as it is simply a linear combination of the other two regressors.\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "s_1 \\\\\n",
    "s_2 \\\\\n",
    "s_3 \\\\\n",
    "s_4 \\\\\n",
    "s_5 \\\\\n",
    "s_6\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "=\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "1 & 0\\\\\n",
    "1 & 0\\\\\n",
    "1 & 0\\\\\n",
    "0 & 1\\\\\n",
    "0 & 1\\\\\n",
    "0 & 1\n",
    "\\end{bmatrix}\n",
    "\\begin{bmatrix}\n",
    "b_0 \\\\\n",
    "b_1\n",
    "\\end{bmatrix}\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-03T23:53:36.876219Z",
     "start_time": "2020-05-03T23:53:36.742170Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "betas: [9.41009741 4.34939829]\n",
      "beta1 + beta2: 13.759495698990353\n",
      "beta1 - beta2: 5.060699128393246\n",
      "mean(group1): 9.410097413691801\n",
      "mean(group2): 4.3493982852985535\n",
      "mean(group1) - mean(group2): 5.060699128393248\n",
      "mean(y): 6.879747849495177\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "group1_params = {'n':20, 'mean':10, 'sd':2}\n",
    "group2_params = {'n':20, 'mean':5, 'sd':2}\n",
    "group1 = group1_params['mean'] + np.random.randn(group1_params['n']) * group1_params['sd']\n",
    "group2 = group2_params['mean'] + np.random.randn(group2_params['n']) * group2_params['sd']\n",
    "\n",
    "y = np.hstack([group1, group2])\n",
    "x = pd.DataFrame({'Group1':np.hstack([np.ones(len(group1)), np.zeros(len(group2))]), 'Group2':np.hstack([np.zeros(len(group1)), np.ones(len(group2))])})\n",
    "\n",
    "run_regression_simulation(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model is obviously separately estimating the means of each group, but how do we know if the difference is significant?  Any ideas?\n",
    "\n",
    "Just like the single subject regression models, we would need to calculate a contrast, which would simply be $c=[1 -1]$. \n",
    "\n",
    "All three of these different approaches will yield identical results when performing a hypothesis test, but each is computing the t-test slightly differently."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Paired-Samples T-Test\n",
    "\n",
    "Now let's demonstrate that a paired-samples t-test can also be computed using a regression. Here, we will need to create a long format dataset, in which each subject $s_i$ has two data points (one for each condition $a$ and $b$). One regressor will compute the contrast between condition $a$ and condition $b$. Just like before, we need to account for the mean, but instead of computing a grand mean for all of the data, we will separately model the mean of each participant by adding $n$ more binary regressors where each subject is indicated in each regressor.\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "s_1a \\\\\n",
    "s_1b \\\\\n",
    "s_2a \\\\\n",
    "s_2b \\\\\n",
    "s_3a \\\\\n",
    "s_3b\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "=\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "1 & 1 & 0 & 0\\\\\n",
    "-1 & 1 & 0 & 0\\\\\n",
    "1 & 0 & 1 & 0\\\\\n",
    "-1 & 0 & 1 & 0\\\\\n",
    "1 & 0 & 0 & 1\\\\\n",
    "-1 & 0 & 0 & 1\n",
    "\\end{bmatrix}\n",
    "\\begin{bmatrix}\n",
    "\\beta_0 \\\\\n",
    "\\beta_1 \\\\\n",
    "\\beta_2 \\\\\n",
    "\\beta_3\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "This simulation will be slightly more complicated as we will be adding subject level noise to each data point. In this simulation, we will assume that $\\epsilon_i = \\mathcal{N}(30, 10)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-03T23:53:38.895275Z",
     "start_time": "2020-05-03T23:53:38.720266Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "betas: [38.56447613 35.08912938 42.0287038  22.7343386  28.18762639 42.88390721\n",
      "  7.0837409  43.58511337 22.66066626 42.75568132 37.21848932 19.9169548\n",
      " 36.84854258 30.06708857 44.8072347  34.76752398 34.36573871 42.14435925\n",
      " 22.91057627 52.88492285]\n",
      "contrast beta: 2.545226964528254\n",
      "mean(subject betas): 36.62046768344662\n",
      "mean(y): 36.620467683446634\n",
      "mean(a): 39.16569464797489\n",
      "mean(b): 34.07524071891838\n",
      "mean(a-b): 5.090453929056513\n",
      "sum(a_i-mean(y_i))/n: 2.5452269645282555\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a_params = {'mean':10, 'sd':2}\n",
    "b_params = {'mean':5, 'sd':2}\n",
    "sample_params = {'n':20, 'mean':30, 'sd':10}\n",
    "\n",
    "y = []; x = []; sub_id = [];\n",
    "for s in range(sample_params['n']):\n",
    "    sub_mean = sample_params['mean'] + np.random.randn()*sample_params['sd']\n",
    "    a = sub_mean + a_params['mean'] + np.random.randn() * a_params['sd']\n",
    "    b = sub_mean + b_params['mean'] + np.random.randn() * b_params['sd']\n",
    "    y.extend([a,b])\n",
    "    x.extend([1, -1])\n",
    "    sub_id.extend([s]*2)\n",
    "y = np.array(y)\n",
    "\n",
    "sub_means = pd.DataFrame([sub_id==x for x in np.unique(sub_id)]).T\n",
    "sub_means = sub_means.replace({True:1,False:0})\n",
    "X = pd.concat([pd.Series(x), sub_means], axis=1)\n",
    "    \n",
    "run_regression_simulation(X, y, paired=True)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-01T15:18:33.825089Z",
     "start_time": "2019-05-01T15:18:33.819128Z"
    }
   },
   "source": [
    "Okay, now let's try to make sense of all of these numbers. First, we now have $n$ + 1 $\\beta$'s. $\\beta_0$ corresponds to the between condition contrast. We will call this the *contrast $\\beta$*. The rest of the $\\beta$'s model each subject's mean. We can see that the means of all of these subject $\\beta$'s corresponds to the overall mean of $y$.\n",
    "\n",
    "Now what is the meaning of the contrast $\\beta$?\n",
    "\n",
    "We can see that it is not the average within subject difference between the two conditions as might be expected given a normal paired-samples t-test.\n",
    "\n",
    "Instead, just like the independent samples t-test described above, the contrast value reflects the average deviation of a condition from each subject's individual mean.\n",
    "\n",
    "$$\\sum_{i=1}^n{\\frac{a_i - mean(y_i)}{n}}$$\n",
    "\n",
    "where $n$ is the number of subjects, $a$ is the condition being compared to $b$, and the $mean(y_i)$ is the subject's mean.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear and Quadratic contrasts\n",
    "Hopefully, now you are starting to see that all of the different statistical tests you learned in intro stats (e.g., one-sample t-tests, two-sample t-tests, ANOVAs, and regressions) are really just a special case of the general linear model.\n",
    "\n",
    "Contrasts allow us to flexibly test many different types of hypotheses within the regression framework. This allows us to test more complicated and precise hypotheses than might be possible than simply turning everything into a binary yes/no question (i.e., one sample t-test), or is condition $a$ greater than condition $b$ (i.e., two sample t-test). We've already explored how contrasts can be used to create independent and paired-samples t-tests in the above simulations. Here we will now provide examples of how to test more sophisticated hypotheses.\n",
    "\n",
    "Suppose we manipulated the intensity of some type of experimental manipulation across many levels. For example, we increase the working memory load across 4 different levels. We might be interested in identifying regions that monotonically increase as a function of this manipulation. This would be virtually impossible to test using a paired contrast approach (e.g., t-tests, ANOVAs). Instead, we can simply specify a linear contrast by setting the contrast vector to linearly increase. This is as simple as `[0, 1, 2, 3]`. However, remember that contrasts need to sum to zero (except for the one-sample t-test case).  So to make our contrast we can simply subtract the mean - `np.array([0, 1, 2, 3]) - np.mean((np.array([0, 1, 2, 3))`, which becomes $c_{linear} = [-1.5, -0.5,  0.5,  1.5]$.\n",
    "\n",
    "Regions involved in working memory load might not have a linear increase, but instead might show an inverted u-shaped response, such that the region is not activated at small or high loads, but only at medium loads.  To test this hypothesis, we would need to construct a quadratic contrast $c_{quadratic}=[-1, 1, 1, -1]$.\n",
    "\n",
    "Let's explore this idea with a simple simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-03T23:53:40.881430Z",
     "start_time": "2020-05-03T23:53:40.620753Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Inverted U-Response to WM Load')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First let's make up some hypothetical data based on different types of response we might expect to see.\n",
    "sim1 = np.array([.3, .4, .7, 1.5])\n",
    "sim2 = np.array([.4, 1.5, .8, .4])\n",
    "x = [1,2,3,4]\n",
    "\n",
    "# Now let's plot our simulated data\n",
    "f,a = plt.subplots(ncols=2, sharey=True, figsize=(10, 5))\n",
    "a[0].bar(x, sim1)\n",
    "a[1].bar(x, sim2)\n",
    "a[0].set_ylabel('Simulated Voxel Response', fontsize=18)\n",
    "a[0].set_xlabel('Working Memory Load', fontsize=18)\n",
    "a[1].set_xlabel('Working Memory Load', fontsize=18)\n",
    "a[0].set_title('Monotonic Increase to WM Load', fontsize=18)\n",
    "a[1].set_title('Inverted U-Response to WM Load', fontsize=18)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See how the data appear to have a linear and quadratic response to working memory load?\n",
    "\n",
    "Now let's create some contrasts and see how a linear or quadratic contrast might be able to detect these different predicted responses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-03T23:53:43.342050Z",
     "start_time": "2020-05-03T23:53:43.109070Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Contrast: [-1.5 -0.5  0.5  1.5]\n",
      "Quadratic Contrast: [-1  1  1 -1]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Inverted U-Response to WM Load')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LaLE+idZn5o+VZFXtl+SLtNO5j6UdVe+d5DNVtUfP8ot2mna6zzysAlhoR9KSixW004HQX5EeTTtNTFfmEnouAprGAbQj64/Qflw/PZ5Q113XAncM7Qf8vcCptCTxRtrpp7+YfuoFM6d9o6pOTXIS8Jwkr6WdUns68I2q+tXANKNug8P2pZlMxfVNFq4V8Jru71r3soN28dZAOWh93R47WHTg/ak9raeHJzmTtu2+EdhnYJo51StV9e9JDqDVFX9G6/P6yiTvqKp/m2b6cZvrdvXF7qzIk2jf1c7APwDfSbJzd3A8Vf65tIaJYYYd6Cy0I4HXsaZem+p/OOVo4D1dvbCCVg8cM8s8D6Cdmj+YdgA+3RnJBdO14H+Dlri/j9byfTltne9Jq7vH2ui3USSIVXVDkoNolf81zPwjNbVB35d2YUSv+wyUGcXFtB+h+w6OSHJb2tHpSb1hj2k+62uqFeGBjN6iMO7lr6upiy5umO2UWddx/im0I8IXDowberqka/H4KK3T8Ca0o8tnJXlXdyr/bNotEH7RndJYF9NtD9C2xyck2WJIN4D70CrFuVxINHVaZSdaxfLTWnNhF7SK9F+T7ERrXfx876mfaXyDdhHILrS+SuNuGZoyyn77ONp97f6+qga7IwzeVHdqmnuxtvsMGTYJn6CdFtqJtv9vxtoXPYxjG5wyU910Ga0P6FxOTZ9LO7OzSW/rS9d6Pdd7LP6s+3vvacbfe6AcwL/S30o8q6r6n7Qrc1+WZL/u9P+c65We+ZwLfAD4QNpFW1+n3TrnXVV10ZC4ew1ux73b/GxlR9adAfhv4L+75ONtwKtopzQ/y5rP/5tRuiIMLmaGcefSEpp7s/aB6NTn+xmz+z9aX8mdaHXhNbTb30w5mpbrrKC1Lp40zdmYNUFX/TjJ/9Hqte9Xu1XYfBhlHe9I65/5xqp6fW/BrH0Hjqnvbb3qtY3lFDO0/kJvAF7Y20dpiCNoTbMvSc99v7r/X0Lrt3fENNNOq+sr9yXgQUmeMDB6H9p3/YWeYVNXct1uPeezvj5Ha/l5/VT/hV5dxTGfTqa1bPx9kmFJ8VyW/yNaB/8XZvitNjZNMvU9T/1QZaDMtrQj/t5ht8zAbYW6H7qpymxqnp/s/r4lQ+6hlmQup2GGbg+dQ2nrfZ/egUmeCDwIOGwufUW702A/o7UYrGDtq/i+Qzu6XtW9P2oO87yRdtXhG5jfVqVR9tvp1vGu9Pc/hHaF/PnAnum5pVO3L8x6K48F8ilap/3ndq/LgS8OlBnHNjhlprrpYOBhmf5pE739775IOwU5eMuQObemdUnV/9Gu1u+79Ul3OvSl3dtDe6Y5oaq+2fua4+LeQOuP/e/d+znXK2m3/um7LVJ3qncqWR9MWJ+T5E498/oT2tmLG1hz940TaRdQ7dl7mrxbztSFJ4PbwaySbJKBp+R0B4JT3Xym1vshtFPvb0iy1lXn3Wce9pCKXrPVawCv7q3nu36RTwW+W1WzXjVcVb+nbSMPoR38/1/XAjrlNNrZkFcy/f0Ph9mHtk28eo7l18Uo63i6eu1+DFyXUFW/pvUJ3i09tzjq2c7mZKNoQQSoql+w5odtpnKXJXkV7SrkH2TNPQFXAncHXtB1UF8Xr6EdcRya5EO0Ky7/nNaH4Rj6j/qP7f6+PcnBtCOg07oO/qPMZ71U1flJXkr7Pk7tWmJ/Trtdw260Pp3jbLEcXH4l2ZPWKvTDJFO3udmClsh8jXZEPts8/o7WUfeU7jTP6bTTUnenXbj0atoVllcm+Qbwt0mmjjS3p9226Gf099O5B3B0ki90Mf2WdrT7oq7sd7rlH5d2n7hVwElJPgv8ktba8xDaaZw/meWrOIPWcvxPSX5Ha6m5qKq+Tbty+HnAv3WnhY7pPtc/0TrZj/L0hyNp63Qb1vS5pPsclyU5hbatTZWdVVUdRuuLuL6env6LMab8tKo+PcJ++126W0Z039f5tBbqv6Odbv5jotGdfXgZ7cfwh0k+QkvG/p72o7LdGD7Xeqmqi5J8lda94ebAxwb7mY1pG5wyU930WlorzCFJDunKXkfbh55ES7hXdtO/g3ba6yNpt4s5nXZg8khGu3XW3rSDmWOTfJSWdG1BSyIeSWu5HvmgflBVHZnke8Dzkrylqs6da71Ca73aP8n/0q4ovYr2vT8f+MGQFqizaNvxfrT9/tm0CyjeVFXndfHckGRvWoPAcUn278r+DfAI2hXrZzO6zWj9JA+jJYUX0frCvYhWx32pW/75SV5EO3tyZpJP0n4btqbtQ0+jtUatnm5BVXVJknOAPZL8lFZfXV1VX6qqI7ptaA/gtkm+zJrb3FxLu5XQXB1JWwePYs19PKdiqCTfYc09W+darx3D7Kei5+JxXWvyoN90XZjmuo7PpG1/r+oaLn5C+416Aa1ee8jA/F9OS4a/l+SDrLnNzdzzvprDrQEW24s5XD7eU7bvNjc9w3enXQZ+dff6PvC0IeVWTzP9VAwrB4bfhXY0fxGt4jyXdruHWw6Zx6u68X9g4LLzuc6HmW9JsVbsDNzmpmf4rrQWmMtpO+e5tH5lW466Lpj51gZD46U90u2/aT/s19F+3A4FHjzbuugZvz2tJXl1N49LaD9YbwXu3FNuK1ql98vus55Ku5XMyi62FV25LWmn9k6i7VzX0JL19wLbDln+k2mnlC6lHXmfR+uf98I5btdPoh1RXsvArTtoR75v7dbLdd128Ulg+xH3nb9lzS1O1pqWNbcguYieW0/0jP94N36rWZazLre5me71tXXYb3ekHVxMXaRyFK1f2MfpGkwGyv9Vt56n1tubWHMbjlk/w3zuG924p/d8H4+eYZlz2gaZph7oGT9T3XRL2n0WT+32iStpP14fAR4+MJ/taGcpruheX6Ldcmk1c7w1TTefe3Tr7rwupitpyekLmeVWXNNsa0NvqQQ8vht/YM+wWesVWn29X/c9XNFtm2fS+jTeZsg2sZKWAJ3draezgX+ZJqbH0urmK2h1w4/oudVZT7mjgNVDhvdtc7QDhbfSbndySbf81bR+djsMmf7RtARm6rfol7Qk61+Bm8/hO38Y7S4JV3dxrO4ZtymtRfnMLo5LafX+/Ufc1x7Dmv3jsUPGv7Qbd33v+hiy7y2fZTnrcpub6V4/Xod1vD3t9P/FtD7EP6TViVPxLxso/+e0OvJaWnL+QdpFl3P6DOlmIkmS5lHa00WOpN0b8uOTjUaa2cbUB1GSJEljYIIoSZKkPiaIkiRJ6mMfREmSJPWxBVGSJEl9Npr7IM7VVlttVcuWLZt0GJIW0AknnPCbqtp60nGMg3WYtLRMqv5acgnismXLOP744ycdhqQFlOTnk45hXKzDpKVlUvWXp5glSZLUxwRRkiRJfRZtgpjkzkmOTHJGktOT/MuQMkny/iTnJDklyYMnEaskSdLGZDH3Qbwe+NeqOjHJZsAJSY6oqjN6yjwR2KF7PRz4cPdXkiRJ62jRtiBW1YVVdWL3/9TD4O84UGw34KBqjgW2SLLtAocqSZK0UVm0CWKvJMuABwE/GBh1R+C8nvfns3YSKUmSpBEs+gQxya2B/wVeWlVXrOM89kpyfJLjL7744vEGKEnzzDpM0kJb1AlikpvSksODq+rzQ4pcANy55/2dumF9qmr/qlpeVcu33nqjuFeupCXEOkzSQlu0CWKSAB8Dzqyqd09T7DDgud3VzI8ALq+qCxcsSEmSpI3QYr6K+dHA3wGnJjmpG/YaYDuAqtoP+ArwJOAc4HfAnhOIU5IkaaOyaBPEqvoukFnKFPDi+Yxj2T6Hz+fstY5Wv+3Jkw5BkqSN1qI9xSxJkqTJMEGUJElSHxNESZIk9TFBlCRJUh8TREmSJPUxQZQkSVIfE0RJkiT1MUGUJElSHxNESZIk9TFBlCRJUh8TREmSJPUxQZQkSVIfE0RJkiT1MUGUJElSHxNESZIk9TFBlCRJUh8TREmSJPUxQZQkSVIfE0RJkiT1MUGUJElSHxNESZIk9TFBlCRJUh8TREmSJPUxQZQkSVIfE0RJkiT1MUGUJElSHxNESZIk9TFBlCRJUh8TREmSJPUxQZQkSVIfE0RJkiT1MUGUJElSHxNESZIk9TFBlCRJUh8TREmSJPUxQZQkSVIfE0RJkiT1MUGUJElSn0WdICY5IMlFSU6bZvyKJJcnOal7vW6hY5QkSdrYbDrpAGbxcWBf4KAZynynqp6yMOFIkiRt/BZ1C2JVHQNcOuk4JEmSlpLF3oI4F49McjLwS+AVVXX6YIEkewF7AWy33XYLHJ4krR/rMK2LZfscPukQNI3Vb3vypEOY1aJuQZyDE4Htq+oBwAeAQ4cVqqr9q2p5VS3feuutFzRASVpf1mGSFtoGnSBW1RVVdVX3/1eAmybZasJhSZIkbdA26AQxyTZJ0v3/MNrnuWSyUUmSJG3YFnUfxCSfBlYAWyU5H3g9cFOAqtoPeAbwoiTXA9cAe1RVTShcSZKkjcKiThCr6lmzjN+XdhscSZIkjckGfYpZkiRJ42eCKEmSpD4miJIkSepjgihJkqQ+JoiSJEnqY4IoSZKkPiaIkiRJ6mOCKEmSpD4miJIkSepjgihJkqQ+JoiSJEnqM/KzmJPcCngkcAfgm1X167FHJUmSpIkZqQUxyYuAC4BvAAcB9+2G3z7JtUn+cfwhSpIkaSHNOUFM8nTgg8CRwPOBTI2rqouArwFPG3eAkiRJWlijtCC+EjiyqnYHvjhk/PHA/cYSlSRJkiZmlATx/sAXZhh/IXD79QtHkiRJkzZKgnjDLOX/FLh6/cKRJEnSpI2SIJ4MPH7YiCQ3Af4aOG4cQUmSJGlyRkkQ9wWemORNwO2mpk9yT+CztCua3z/m+CRJkrTA5nwfxKr6TJL7A68FXt0N/hrtauYAq6rqq+MPUZIkSQtppBtlV9W/J/k88BzgXrTE8Gzgk1V1/DzEJ0mSpAU28pNUqupE4MR5iEWSJEmLgM9iliRJUp85tyAmOWAOxaqq/mE94pEkSdKEjXKKeeUcyhRggihJkrQBm/Mp5qq6yeALuClwT+AjwLHAbecpTkmSJC2Q9eqDWFU3VNXZVfUC4BLg7eMJS5IkSZMyzotUvgY8fYzzkyRJ0gSMM0G8HXDrMc5PkiRJEzDyfRAHJdkC2Bl4GXDCekckSZKkiRrlNjc30q5SHjoauBR4+TiCkiRJ0uSM0oJ4EGsniEVLDM8CPl1VV44rMEmSJE3GnBPEqlo5j3FIkiRpkfBRe5IkSeozbQtiku3WZYZV9Yt1D0eSJEmTNtMp5tVMf1HKTDZZt1AkSZK0GMyUIL6RdUsQJUmStAGbNkGsqlULGMdQSQ4AngJcVFX3GzI+wPuAJwG/A1ZW1YkLG6UkSdLGZbFfpPJx4AkzjH8isEP32gv48ALEJEmStFFbpyepJLk1sAVDEsxxXqRSVcckWTZDkd2Ag6qqgGOTbJFk26q6cFwxSJIkLTUjJYhJ9gD+Hbj3DMUW8iKVOwLn9bw/vxtmgihJkrSO5nyKOcnTgE/Rksr/oj1e79PAZ4E/0J7D/MZ5iHG9JdkryfFJjr/44osnHY4kjcQ6TNJCG6UP4iuAM4EHAq/rhh1QVXsAy4F7AieNN7xZXQDcuef9nbphfapq/6paXlXLt9566wULTpLGwTpM0kIbJUHcEfhEVV0L3NgN2wSgqk4D9gdePd7wZnUY8Nw0jwAut/+hJEnS+hmlD+ImwCXd/9d0f2/TM/4nwIvGEdSUJJ8GVgBbJTkfeD1wU4Cq2g/4Cu0WN+fQbnOz5ziXL0mStBSNkiCeD2wPUFXXJLkIeAjwuW78PYGrxxlcVT1rlvEFvHicy5QkSVrqRkkQvw/szJr+h4cBL01yDe1U9YuBL403PEmSJC20URLEDwG7J7lFVV0DvBZ4GLCqG3867UIWSZIkbcBmTBCT7Ad8rKqOq6rjgOOmxlXVxcADk+wI3ACcWVU3TjMrSZIkbSBmu4p5L9oTSk5J8tIkWw0WqKpTqup0k0NJkqSNw2wJ4uOAg4G7Au8Gzk/y2SRPSJJ5j06SJEkLbsYEsaqOrKrnAtsALwBOBJ4OHA78Ismbktxt/sOUJEnSQpnTjbKr6qqq+khVPQq4F/DObvS6tV4AABXASURBVNrXAmclOTLJ3ya5+TzGKkmSpAUwypNUAKiqs6pqH9oj7v4S+ALwSOATgE8xkSRJ2sCNcpubPt1FKYcn+S3tKSu7AZuPKzBJkiRNxjoliEm2AZ4HrATuAQQ4AfjY2CKTJEnSRMw5QUyyKa2VcE9g127aS4F9afdKPGVeIpQkSdKCmjVBTPIAWlL4bGDLbvA3aa2Fh1bVdfMXniRJkhbabE9SORF4AO0U8s+BDwIHVtUvFiA2SZIkTcBsLYj3AQ6htRZ+q6pq/kOSJEnSJM2WIG5bVb9dkEgkSZK0KMz2JBWTQ0mSpCVm5BtlS5IkaeNmgihJkqQ+JoiSJEnqY4IoSZKkPnNOEJO8Lsn9Zhh/3ySvG09YkiRJmpRRWhBXATvOMP5+wOvXKxpJkiRN3DhPMd8cuH6M85MkSdIEzPaovc2BLXoGbZlkuyFFbwc8BzhvjLFJkiRpAmZ7ksrLgKl+hQW8t3sNE+BVY4pLkiRJEzJbgnhU9ze0RPELwCkDZQq4Cji2qr4/1ugkSZK04GZMEKvqaOBogCTbA/tV1Q8WIjBJkiRNxmwtiH9UVXvOZyCSJElaHEa5D+LDkvzjwLDdkpya5IIkbxl/eJIkSVpoo9zm5vXAU6fedFczfxrYBrgc+LcktjJKkiRt4EZJEB8AfLfn/R60i1ceWFX3Ab4B7DXG2CRJkjQBoySIWwK/7nn/eOCYqrqge38YsMO4ApMkSdJkjJIgXgbcASDJzYBHAMf0jC/gFuMLTZIkSZMw56uYgZOA5yf5JrA77dF6X+8Zfxf6WxglSZK0ARolQXwTrZ/hD2l9D4+oquN7xj8F8B6JkiRJG7hR7oP4/SQPpvU9vBz4n6lxSbakJY9fGHuEkiRJWlCjtCBSVWcBZw0Zfgntuc2SJEnawI1ykYokSZKWgJESxCR3S7JvkuOSnJPk3IHXT8cZXJInJPlJt6x9hoxfmeTiJCd1r+ePc/mSJElL0ZxPMSe5P+1G2TcDfgLcFTiddn/EbYCfAuePK7AkmwAfBHbp5ntcksOq6oyBop+pqr3HtVxJkqSlbpQWxDcC19GeqPK4bti/VNWfAi8AtgBePMbYHgacU1XnVtV1tItidhvj/CVJkjTEKAniY4D9q+ontJtiQ7vdDVX1EeCrwNvGGNsdgfN63p/fDRv09CSnJPlckjuPcfmSJElL0igJ4ma008jQWhIBbtUz/nu0JHIhfQlYVlU7AkcAnxhWKMleSY5PcvzFF1+8oAFK0vqyDpO00EZJEH9N62tIVV0JXA3co2f8bYFNxhcaFwC9LYJ36ob9UVVdUlW/795+FHjIsBlV1f5Vtbyqlm+99dZjDFGS5p91mKSFNuqj9pb3vD8a+JckP6QlmnsDJ48xtuOAHZLchZYY7gE8u7dAkm2r6sLu7VOBM8e4fEmSpCVplATxU8CLk9yiqq4B/oOWJB7Zjb8GeM24Aquq65PsTXve8ybAAVV1epI3AsdX1WHAPyd5KnA9cCmwclzLlyRJWqpGedTeZ4DP9Lz/UZL7ArsDNwBfrapzxxlcVX0F+MrAsNf1/P9q4NXjXKYkSdJSN6cEMcnNgIcDF1bV2VPDq+o84P3zFJskSZImYK4XqdwAfAt44jzGIkmSpEVgTgliVV0P/IruvoeSJEnaeI1ym5vPAs9MMtLzmyVJkrRhGeUq5o8COwFHJHkvcDbwu8FCVfWLMcUmSZKkCRglQTyN9oi9ACtmKDfOm2VLkiRpgY2SIL6RNc9gliRJ0kZqlPsgrprHOCRJkrRIzPmCkySvS3K/GcbfN8nrphsvSZKkDcMoVySvAnacYfz9gNevVzSSJEmauHHesubmtGciS5IkaQM2Yx/EJJsDW/QM2jLJdkOK3g54DnDeGGOTJEnSBMx2kcrLgKl+hQW8t3sNE+BVY4pLkiRJEzJbgnhU9ze0RPELwCkDZQq4Cji2qr4/1ugkSZK04GZMEKvqaOBogCTbA/tV1Q8WIjBJkiRNxij3QdxzPgORJEnS4jDKk1QASLIDsAOwJe3Uc5+qOmgMcUmSJGlC5pwgJrkD8Algl6lBQ4oVYIIoSZK0ARulBXFfWnL4YeDbwCXzEpEkSZImapQEcRfaRSp7z1cwkiRJmrxRnqRyE+Dk+QpEkiRJi8MoCeJ3gAfMVyCSJElaHEZJEF8O7J7k6fMVjCRJkiZvlD6IH6Y9MeWQJL8EzgVuGChTVfW4cQUnSZKkhTdKgnhX2m1sftG932784UiSJGnSRnmSyrJ5jEOSJEmLxCh9ECVJkrQErMuj9jYHdqadcobWF/GIqrpynIFJkiRpMkZKEJM8H3gXcGvWPGqvgKuSvLyqPjbm+CRJkrTARnkW81OB/Wkthv8BnN6Nui/wEmD/JBdV1ZfGHqUkSZIWzCgtiK8CzgQeXlVX9Qz/VpIDgWOBfwNMECVJkjZgo1yk8gDg4wPJIQBd/8NP4JNWJEmSNnijJIiZZXytTyCSJElaHEZJEE8GVia51eCIJLcGVnZlJEmStAEbpQ/iO4HPAycmeT9wRjd86iKVuwN/Nd7wJEmStNBGeZLKoUn2Bt4OfIA1p5QDXA3sXVVfHH+IkiRJWkgj3Qexqj6U5FPALsBdusFTN8q+fNzBSZIkaeGN/CSVqroM+Ow8xLKWJE8A3gdsAny0qt42MP5mwEHAQ4BLgL+pqtULEZskSdLGasaLVJJskuRtSV44S7kXJXlLktmudJ6zJJsAHwSeCNwHeFaS+wwU+wfgt1V1d+A9tNPfkiRJWg+zXcX8t8ArgeNmKfdD2k2ynzWOoDoPA86pqnOr6jrgf4DdBsrsRrv/IsDngMeNM0mVJElaimZLEJ8JfLOqTpipUDf+64w3QbwjcF7P+/O7YUPLVNX1wOXAlmOMQZIkacmZrQ/iQ4B3zXFeRwIvX79w5keSvYC9ALbbbruRpl39tifPR0jaQCzb5/BJh6BpLKV9c13rMLffxWshtt+ltI9o/GZrQbwdcNEc53VxV35cLgDu3PP+Tt2woWWSbArchnaxSp+q2r+qllfV8q233nqMIUrS/LMOk7TQZksQrwS2muO8tgTWek7zejgO2CHJXZL8CbAHcNhAmcOA53X/PwP4dlX5yD9JkqT1MFuCeDqw6xzntUtXfiy6PoV70/o2ngkcUlWnJ3ljkqd2xT4GbJnkHNrp7X3GtXxJkqSlarY+iJ8H3pVkt5mektIlbLsw5j6IVfUV4CsDw17X8/+1wF+Pc5mSJElL3WwtiP8FnAMckuTNSZb1jkyyLMl/AocAZ3XlJUmStAGbsQWxqq5J8mTgy8CrgX2SXEHrm7gZsDntWcw/AZ7StehJkiRpAzZbCyJVdQ7wQOBfgO8CNwDbdH+/0w1/cFX9dB7jlCRJ0gKZ07OYu5bBD3QvSZIkbcRmbUGUJEnS0mKCKEmSpD4miJIkSepjgihJkqQ+JoiSJEnqY4IoSZKkPiaIkiRJ6mOCKEmSpD4miJIkSepjgihJkqQ+JoiSJEnqY4IoSZKkPiaIkiRJ6mOCKEmSpD4miJIkSepjgihJkqQ+JoiSJEnqY4IoSZKkPiaIkiRJ6mOCKEmSpD4miJIkSepjgihJkqQ+JoiSJEnqY4IoSZKkPptOOgBJ0vxY/bYnTzoESRsoWxAlSZLUxwRRkiRJfUwQJUmS1McEUZIkSX1MECVJktTHBFGSJEl9TBAlSZLUxwRRkiRJfUwQJUmS1GdRJohJbpfkiCRnd39vO025G5Kc1L0OW+g4JUmSNkaLMkEE9gG+VVU7AN/q3g9zTVU9sHs9deHCkyRJ2ngt1gRxN+AT3f+fAJ42wVgkSZKWlMWaIN6hqi7s/v8VcIdpyt08yfFJjk1iEilJkjQGm05qwUm+CWwzZNRre99UVSWpaWazfVVdkOSuwLeTnFpVPx2yrL2AvQC222679YxckhaWdZikhTaxBLGqdp5uXJJfJ9m2qi5Msi1w0TTzuKD7e26So4AHAWsliFW1P7A/wPLly6dLNiVpUbIOk7TQFusp5sOA53X/Pw/44mCBJLdNcrPu/62ARwNnLFiEkiRJG6nFmiC+DdglydnAzt17kixP8tGuzL2B45OcDBwJvK2qTBAlSZLW08ROMc+kqi4BHjdk+PHA87v/vw/cf4FDkyRJ2ugt1hZESZIkTYgJoiRJkvqYIEqSJKmPCaIkSZL6mCBKkiSpjwmiJEmS+pggSpIkqY8JoiRJkvqYIEqSJKmPCaIkSZL6mCBKkiSpjwmiJEmS+pggSpIkqc+mkw5AWsxWv+3Jkw5BkqQFZwuiJEmS+pggSpIkqY8JoiRJkvqYIEqSJKmPCaIkSZL6mCBKkiSpjwmiJEmS+pggSpIkqY8JoiRJkvqYIEqSJKmPCaIkSZL6mCBKkiSpT6pq0jEsqCQXAz+fdBwTshXwm0kHoYlZyut/+6raetJBjIN12JLdhrV01/9E6q8llyAuZUmOr6rlk45Dk+H614bObXhpc/0vLE8xS5IkqY8JoiRJkvqYIC4t+086AE2U618bOrfhpc31v4DsgyhJkqQ+tiBKkiSpjwniBijJVUOGvTDJcycRj8YnyZ2SfDHJ2UnOTbJvkpuNYb4rknx5xGmWJXl2z/vlSd6/vrFoabP+2rhZh208TBA3ElW1X1UdNF/zT+P2Mo+SBPg8cGhV7QDsANwCeMc8LnPTGUYvA/5YuVbV8VX1z/MVi5Yu66+Ng3XYxsUdZiORZFWSV3T/H5Xk7Ul+mOSsJH/WDd8kyTuTHJfklCQv6IbfOsm3kpyY5NQku3XDlyX5SZKDgNOAO0/q8y0RfwFcW1UHAlTVDcDLgOcm2TvJvlMFk3w5yYru/w8nOT7J6Une0FPmCUl+nORE4K96hq9K8skk3wM+2a3n73Tr/8Qkj+qKvg34syQnJXlZ7xF8t80c2G0vpyR5+vx+NdqYWX9tNKzDNiIzZd7asG1aVQ9L8iTg9cDOwD8Al1fVQ7sm/+8l+QZwHrB7VV2RZCvg2CSHdfPZAXheVR07iQ+xxNwXOKF3QLdOVjPzvvraqro0ySbAt5LsCJwFfIRWYZ8DfGZgmvsAj6mqa5LcEtilqq5NsgPwaWA5sA/wiqp6CrRTPD3T/wdtW7p/N+626/KBpWlYf22YrMM2IiaIG6/Pd39PoDWzA+wK7JjkGd3729Aq0POBtyT5c+BG4I7AHboyP7dyXfSemWQv2v68La3ivAnws6o6GyDJfwN79UxzWFVd0/1/U2DfJA8EbgDuMYdl7gzsMfWmqn673p9CWsP6a2mxDluETBA3Xr/v/t7AmvUc4CVV9fXegklWAlsDD6mqP3RHezfvRl89/6GqcwbwjN4BSTYHtgEuob/Su3k3/i7AK4CHVtVvk3ycNetuJr3r9WXAr4EH0Crla9cxfmlcrL82TNZhGxH7IC4tXwdelOSmAEnukeRWtCPxi7rKdSdg+0kGuYR9C7hluqs5u9Mt7wL2BX4GPDDJTZLcGXhYN83mtIry8iR3AJ7YDf8xsCzJ3br3z5phubcBLqyqG4G/Azbphl8JbDbNNEcAL5564+kZLQDrr8XPOmwjYoK4YbplkvN7Xi+f43QfpR3hnZjkNOC/aEfnBwPLk5wKPJe2Y2qBVbtr/e7AM5KcTTvivrGq3gx8j1bBngG8Hzixm+Zk4Ee0dfaprhxVdS3tdMzhXQfvi2ZY9IeA5yU5GbgXa47MTwFuSHJykpcNTPOfwG2TnNZNt9N6fXgtJdZfGynrsI2LT1KRFqnuSrxP0zrgnzjpeCRpFNZhGzYTREmSJPXxFLMkSZL6mCBKkiSpjwmiJEmS+pggSpIkqY8JoiRJkvqYIGpeJbllkpd2D1K/NMkfkvw6yVeSrEwy70/z6R7QvirJFvO9rA0pFkkzs/5avLFo/pkgat4kuTvtBqjvoT366K20G5++m/bszAOBtyxAKCuA1wOLoVJbweKJRdI0rL+GWsHiiUXzzGcxa14kuQXwZeCuwNOr6vMDRd6e5KHAQxc8uFl0j/LapLuTv6QlxvpLsgVR8+f5wD2Bdw2pXAGoquOq6kO9w5I8Lcn3klyd5Kru/90Gp02yOslRSe6V5PAkVya5PMnnkmzTU+7jtCNegJ8lqe61qhu/qnt/3yTvTnI+rbXgEd34v0lyWJJfJPl9kt8kOTTJjkNielSSryb5VZJrk1zQnYp6xFxikbRoWH9Zfy15tiBqvjyj+7v/XCdI8k/AB2nP5HxjN3glcGiSF1TV4LzuCBwFfAF4JfAA4AW0h7/v2pX5r+797sDLgN90w08ZmNfBwDW0B8sXcGE3fG/a80T3B34F3I12mul7SR5cVWd3sd+T9vD3XwHvA34N3AF4TBfXsSPEImmyrL+sv1RVvnyN/UWrlC4fofxtgauAc4DNe4ZvDvwUuBLYomf4alpF+MyB+XywG37PnmGrumHLhix3atxRwKZDxt9qyLB7A78HPtQz7J+7+Txsls85bSy+fPlaHC/rr2k/p/XXEnp5ilnzZXNapThXuwC3At5fVVdMDez+fz9wa2DngWl+WVWHDAz7dvd3h9HC5b1Vdf3gwKq6GiDN5km2Ai4GfgI8vKfo5d3f3ZLcfMRlS1pcrL+05Jkgar5cAWw2Qvm7dH9PHzJuathdB4afO6TsJd3fLUdYNsBZwwYmeVCSL9N+LC6nVa4XA/entRpM+R/gm8BrgEuTfDvJvyXZfsQ4JE2e9Zf115Jngqj5chqweZLBSnGcbphhXEac1+/WmkGyHXAM8CDgTbS+N7vSWgtOp2f/qarfV9UutKPyt3axvRH4cZLdR4xF0mRZf1l/LXlepKL58r/An9OuBnzNHMpPHU3fF/jWwLj7DJQZVa3jdLvTTg09taqO7B2RZEtaP57+BVX9EPhhV+bOtPuo/SetI/r6xCJp4Vh/WX8tebYgar58lNbP5RXDbvMAkOQh3ZV/0K6guxp4SZLNespsBryE1gH8iHWM5aru7+1GnG7qCL/vaD7JPwLbDAzbasj059NO5/Qud11jkbRwrL+sv5Y8WxA1L6rqd0meAhxOu83DN2gV5CXA1sBOwOOBd3TlL0vyKtpVfD/o7rkF7TYRdwdeUFWXs26O7f6+PcnBtPuEnVZVp80y3Vdpp24+mWRf4LfAo4En0a5M7N1//j3JrrSb6/6MVin/JXCvqc+4nrFIWiDWX9Zfwtvc+JrfF3BL2j2zvkuroP5Au8fW4cDf0e7431t+d+D7tKPxq7v/nzZkvquBo4YMX0E7DbJyYPiraKd4/tCNX9UNX8UMt22gnWb6Lq2T92Vd3Pej3VZi9cByP9PFdQ1wKfAD2imqzCUWX758La6X9Zf111J+pVvhkiRJEmAfREmSJA0wQZQkSVIfE0RJkiT1MUGUJElSHxNESZIk9TFBlCRJUh8TREmSJPUxQZQkSVIfE0RJkiT1MUGUJElSn/8P+6rsvnT1UuUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First let's create some contrast codes.\n",
    "linear_contrast = np.array([-1.5, -.5, .5, 1.5])\n",
    "quadratic_contrast = np.array([-1, 1, 1, -1])\n",
    "\n",
    "print(f'Linear Contrast: {linear_contrast}')\n",
    "print(f'Quadratic Contrast: {quadratic_contrast}')\n",
    "\n",
    "# Now let's test our contrasts on each dataset.\n",
    "sim1_linear = np.dot(sim1, linear_contrast)\n",
    "sim1_quad = np.dot(sim1, quadratic_contrast)\n",
    "sim2_linear = np.dot(sim2, linear_contrast)\n",
    "sim2_quad = np.dot(sim2, quadratic_contrast)\n",
    "\n",
    "# Now plot the contrast results\n",
    "f,a = plt.subplots(ncols=2, sharey=True, figsize=(10,5))\n",
    "a[0].bar(['Linear','Quadratic'], [sim1_linear, sim1_quad])\n",
    "a[1].bar(['Linear','Quadratic'], [sim2_linear, sim2_quad])\n",
    "a[0].set_ylabel('Contrast Value', fontsize=18)\n",
    "a[0].set_xlabel('Contrast', fontsize=18)\n",
    "a[1].set_xlabel('Contrast', fontsize=18)\n",
    "a[0].set_title('Monotonic Increase to WM Load', fontsize=18)\n",
    "a[1].set_title('Inverted U-Response to WM Load', fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the linear contrast is sensitive to detecting responses that monotonically increase, while the quadratic contrast is more sensitive to responses that show an inverted u-response. Both of these are also signed, so they could also detect responses in the opposite direction.\n",
    "\n",
    "If we were to apply this to real brain data, we could now find regions that show a linear or quadratic responses to an experimental manipulation across the whole brain. We would then test the null hypothesis that there is no group effect of a linear or quadratic contrast at the second level. \n",
    "\n",
    "Hopefully, this is starting you a sense of the power of contrasts to flexibly test any hypothesis that you can imagine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Which regions are more involved with visual compared to auditory sensory processing?\n",
    " - Create a contrast to test this hypothesis\n",
    " - run a group level t-test\n",
    " - plot the results\n",
    " - write the file to your output folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-15T19:57:26.582290Z",
     "start_time": "2020-10-15T19:57:24.413469Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Which regions are more involved in processing numbers compared to words?\n",
    " - Create a contrast to test this hypothesis\n",
    " - run a group level t-test\n",
    " - plot the results\n",
    " - write the file to your output folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-15T20:04:25.759108Z",
     "start_time": "2020-10-15T20:04:25.749874Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Are there gender differences? \n",
    "In this exercise, create a two sample design matrix comparing men and women on arithmetic vs reading.\n",
    "\n",
    "You will first have to figure out the subjects gender using the using the `participants.tsv` file.\n",
    "\n",
    " - Create a contrast to test this hypothesis\n",
    " - run a group level t-test\n",
    " - plot the results\n",
    " - write the file to your output folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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