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ENH: add model specification notebook
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‎notebooks/basic_model_specification.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Model Specification for 1st-Level fMRI Analysis\n",
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"\n",
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"Nipype provides also an interfaces to create a first level Model for an fMRI analysis. Such a model is needed to specify the study specific information, such as **condition**, their **onsets** and **durations**. For more information, make sure to check out [Model Specificaton](http://nipype.readthedocs.io/en/latest/users/model_specification.html) and [nipype.algorithms.modelgen](http://nipype.readthedocs.io/en/latest/interfaces/generated/nipype.algorithms.modelgen.html)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Simple Example\n",
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"\n",
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"Let's consider a simple experiment, where we have three different stimuli such as ``'faces'``, ``'houses'`` and ``'scrambled pix'``. Now each of those three conditions has different stimuli onsets, but all of them have a stimuli presentation duration of 3 seconds.\n",
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"\n",
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"So to summarize:\n",
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"\n",
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" conditions = ['faces', 'houses', 'scrambled pix']\n",
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" onsets = [[0, 30, 60, 90],\n",
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" [10, 40, 70, 100],\n",
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" [20, 50, 80, 110]]\n",
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" durations = [[3], [3], [3]]\n",
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" \n",
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"The way we would create this model with Nipype is almsot as simple as that. The only step that is missing is to put this all into a ``Bunch`` object. This can be done as follows:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"from nipype.interfaces.base import Bunch\n",
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"\n",
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"conditions = ['faces', 'houses', 'scrambled pix']\n",
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"onsets = [[0, 30, 60, 90],\n",
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" [10, 40, 70, 100],\n",
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" [20, 50, 80, 110]]\n",
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"durations = [[3], [3], [3]]\n",
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"\n",
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"subject_info = Bunch(conditions=conditions,\n",
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" onsets=onsets,\n",
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" durations=durations)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"It's also possible to specify additional regressors. For this you need to additionally specify:\n",
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"\n",
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"- **``regressors``**: list of regressors that you want to include in the model (must correspond to the number of volumes in the functional run)\n",
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"- **``regressor_names``**: name of the regressors that you want to include"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Example based on dataset\n",
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"\n",
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"Now for a more realistic example, let's look at a TVA file from our tutorial dataset."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"onset\tduration\ttrial_type\tresponse_time\tcorrectness\tStimVar\tRsponse\tStimulus\tcond\r\n",
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"0.0\t2.0\tincongruent_correct\t1.095\tcorrect\t2\t1\tincongruent\tcond003\r\n",
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"10.0\t2.0\tincongruent_correct\t0.988\tcorrect\t2\t1\tincongruent\tcond003\r\n",
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"20.0\t2.0\tcongruent_correct\t0.591\tcorrect\t1\t1\tcongruent\tcond001\r\n",
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"30.0\t2.0\tcongruent_correct\t0.499\tcorrect\t1\t1\tcongruent\tcond001\r\n",
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"40.0\t2.0\tincongruent_correct\t0.719\tcorrect\t2\t1\tincongruent\tcond003\r\n",
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"52.0\t2.0\tcongruent_correct\t0.544\tcorrect\t1\t1\tcongruent\tcond001\r\n",
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"64.0\t2.0\tcongruent_correct\t0.436\tcorrect\t1\t1\tcongruent\tcond001\r\n",
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"76.0\t2.0\tincongruent_correct\t0.47\tcorrect\t2\t1\tincongruent\tcond003\r\n",
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"88.0\t2.0\tcongruent_correct\t0.409\tcorrect\t1\t1\tcongruent\tcond001\r\n",
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"102.0\t2.0\tincongruent_correct\t0.563\tcorrect\t2\t1\tincongruent\tcond003\r\n",
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"116.0\t2.0\tcongruent_correct\t0.493\tcorrect\t1\t1\tcongruent\tcond001\r\n",
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"130.0\t2.0\tcongruent_correct\t0.398\tcorrect\t1\t1\tcongruent\tcond001\r\n",
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"140.0\t2.0\tcongruent_correct\t0.466\tcorrect\t1\t1\tcongruent\tcond001\r\n",
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"150.0\t2.0\tincongruent_correct\t0.518\tcorrect\t2\t1\tincongruent\tcond003\r\n",
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"164.0\t2.0\tincongruent_correct\t0.56\tcorrect\t2\t1\tincongruent\tcond003\r\n",
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"174.0\t2.0\tincongruent_correct\t0.533\tcorrect\t2\t1\tincongruent\tcond003\r\n",
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"184.0\t2.0\tcongruent_correct\t0.439\tcorrect\t1\t1\tcongruent\tcond001\r\n",
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"196.0\t2.0\tcongruent_correct\t0.458\tcorrect\t1\t1\tcongruent\tcond001\r\n",
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"208.0\t2.0\tincongruent_correct\t0.734\tcorrect\t2\t1\tincongruent\tcond003\r\n",
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"220.0\t2.0\tincongruent_correct\t0.479\tcorrect\t2\t1\tincongruent\tcond003\r\n",
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"232.0\t2.0\tincongruent_correct\t0.538\tcorrect\t2\t1\tincongruent\tcond003\r\n",
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"246.0\t2.0\tcongruent_correct\t0.54\tcorrect\t1\t1\tcongruent\tcond001\r\n",
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"260.0\t2.0\tincongruent_correct\t0.622\tcorrect\t2\t1\tincongruent\tcond003\r\n",
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"274.0\t2.0\tcongruent_correct\t0.488\tcorrect\t1\t1\tcongruent\tcond001\r\n"
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]
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}
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],
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"source": [
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"!cat /data/ds102/sub-01/func/sub-01_task-flanker_run-1_events.tsv"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"So, the only things that we need to specify our model are the onset and the stimuli type, i.e. **column 0** and **column 5 or 7**. Those we can get with the command:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"filename = '/data/ds102/sub-01/func/sub-01_task-flanker_run-1_events.tsv'\n",
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"trailinfo = np.genfromtxt(filename, delimiter='\\t', dtype=None, skip_header=1)\n",
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"trailinfo = [[t[0], t[7]] for t in trailinfo]\n",
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"trailinfo"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Before we can use the onsets, we first need to split them into the two conditions:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"onset1 = []\n",
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"onset2 = []\n",
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"\n",
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"for t in trailinfo:\n",
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" if 'incongruent' in t[1]:\n",
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" onset2.append(t[0])\n",
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" else:\n",
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" onset1.append(t[0])\n",
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"\n",
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"print onset1\n",
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"print onset2"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The last thing we now need to to is to put this into a ``Bunch`` object and we're done:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from nipype.interfaces.base import Bunch\n",
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"\n",
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"conditions = ['congruent', 'incongruent']\n",
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"onsets = [onset1, onset2]\n",
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"durations = [[2], [2]]\n",
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"\n",
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"subject_info = Bunch(conditions=conditions,\n",
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" onsets=onsets,\n",
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" durations=durations)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [default]",
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"language": "python",
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"name": "python2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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