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<title>Course on Practical Neuroimaging in Python — Practical neuroimaging analysis</title>
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<div class="section" id="practical-neuroimaging-the-sequel">
<h1>Practical neuroimaging, the sequel<a class="headerlink" href="#practical-neuroimaging-the-sequel" title="Permalink to this headline">¶</a></h1>
<div class="section" id="who-is-this-course-for">
<h2>Who is this course for?<a class="headerlink" href="#who-is-this-course-for" title="Permalink to this headline">¶</a></h2>
<p>The course is designed for people starting or doing neuroimaging, with some
programming experience (for example, writing MATLAB or Python scripts).</p>
<p>We have designed the course to help you:</p>
<ul class="simple">
<li>Understand the basic concepts in neuroimaging, and how they relate to the
wider world of statistics, engineering, computer science;</li>
<li>Be comfortable working with neuroimaging data and code, so you can write
your own basic algorithms, and understand other people’s code;</li>
<li>Work with code and data in a way that will save you time, help you
collaborate, and continue learning.</li>
</ul>
</div>
<div class="section" id="requirements">
<h2>Requirements<a class="headerlink" href="#requirements" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>Reasonable knowledge of programming in any language;</li>
</ul>
</div>
<div class="section" id="background">
<h2>Background<a class="headerlink" href="#background" title="Permalink to this headline">¶</a></h2>
<blockquote>
<div>That which I cannot build, I do not understand (Feynman)</div></blockquote>
<ul class="simple">
<li>Practical workflow for continuous learning;</li>
<li>Relevant concepts in mathematics / statistics / engineering;</li>
<li>FMRI analysis steps.</li>
</ul>
<p>We aim to teach you to work efficiently, so that you can in due course forget
about your tools and think about the ideas. We aim to make things simple,
rather than easy, so that you can reach a stage where things are both simple
and easy.</p>
<div class="section" id="practical-workflow">
<h3>Practical workflow<a class="headerlink" href="#practical-workflow" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li>Version control (we teach git);</li>
<li>Extensible text editor (see below);</li>
<li>Versatile programming language (we teach Python);</li>
<li>Testing code;</li>
<li>Collaborating with code.</li>
</ul>
</div>
<div class="section" id="relevant-concepts">
<h3>Relevant concepts<a class="headerlink" href="#relevant-concepts" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li>convolution (hemodynamic modeling, smoothing);</li>
<li>interpolation (slice time correction, image resampling);</li>
<li>optimization (registration, advanced statistics);</li>
<li>basic linear algebra (statistics);</li>
</ul>
</div>
<div class="section" id="fmri-analysis-steps">
<h3>FMRI analysis steps<a class="headerlink" href="#fmri-analysis-steps" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li>Diagnostics;</li>
<li>slice timing;</li>
<li>motion correction;</li>
<li>registration within and between subject;</li>
<li>smoothing;</li>
<li>statistical estimation with multiple regression;</li>
<li>statistical inference.</li>
</ul>
</div>
</div>
<div class="section" id="format-of-the-classes">
<h2>Format of the classes<a class="headerlink" href="#format-of-the-classes" title="Permalink to this headline">¶</a></h2>
<ul>
<li><p class="first">Prior reading / homework for each week of approx 30 minutes;</p>
</li>
<li><p class="first">Class is 2 hours:</p>
<blockquote>
<div><ul class="simple">
<li>10 minutes debrief from previous class</li>
<li>30 minutes talk introduction + questions;</li>
<li>60 minutes problems;</li>
<li>10 minutes review of problems;</li>
</ul>
</div></blockquote>
</li>
</ul>
<p>For each day there will be a short teaching point on one of:</p>
<ul class="simple">
<li>version control;</li>
<li>text editing;</li>
<li>code collaboration.</li>
</ul>
</div>
<div class="section" id="general-reading">
<h2>General reading<a class="headerlink" href="#general-reading" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><a class="reference external" href="http://www.nature.com/news/programming-pick-up-python-1.16833">Nature article on Python in science</a></li>
<li><a class="reference external" href="http://norvig.com/21-days.html">Essay by Peter Norvig</a>. <cite>Peter Norvig
<http://en.wikipedia.org/wiki/Peter_Norvig></cite> is director of research at
Google.</li>
<li>Greg Wilson’s article on scientific computing: Wilson, Greg, et al. “Best
practices for scientific computing.” PLoS biology 12.1 (2014): e1001745.</li>
<li>Donoho reproducibility article : Donoho, David L. 2010. An invitation to
reproducible computational research. Biostatistics 11, 385–388.
<a class="reference external" href="http://biostatistics.oxfordjournals.org/content/11/3/385.full">http://biostatistics.oxfordjournals.org/content/11/3/385.full</a></li>
</ul>
</div>
<div class="section" id="text-editors">
<h2>Text editors<a class="headerlink" href="#text-editors" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>vim;</li>
<li>emacs;</li>
<li>TextMate;</li>
<li>sublime text;</li>
<li>Notepad++.</li>
</ul>
<p>You can use any other text editor, but we’ll be doing text editor challenges
through the course to teach ourselves speed and shortcuts for our editors.</p>
<p>The list of editors comes from
<a class="reference external" href="http://www.rackspace.com/blog/text-editor-madness-bracket-vote-for-your-favorite">http://www.rackspace.com/blog/text-editor-madness-bracket-vote-for-your-favorite</a>
with the edition of TextMate (because we know at least one extremely efficient
coder who uses it).</p>
<p>Your teachers use Vim (x2) and Emacs (x1).</p>
</div>
<div class="section" id="general-teaching-points">
<h2>General teaching points<a class="headerlink" href="#general-teaching-points" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>Proceed by iterating through a single subject analysis;</li>
<li>balance of IPython notebook and Python modules.</li>
</ul>
</div>
<div class="section" id="schedule">
<h2>Schedule<a class="headerlink" href="#schedule" title="Permalink to this headline">¶</a></h2>
<div class="section" id="day-0-introduction-and-taster">
<h3>Day 0 : introduction and taster<a class="headerlink" href="#day-0-introduction-and-taster" title="Permalink to this headline">¶</a></h3>
<div class="section" id="prior">
<h4>Prior<a class="headerlink" href="#prior" title="Permalink to this headline">¶</a></h4>
<p>Install requirements on your machine</p>
<ul class="simple">
<li>git;</li>
<li>Python;</li>
<li>pip;</li>
<li>scipy-stack (numpy, scipy, matplotlib, IPython, pandas);</li>
<li>nibabel.</li>
</ul>
<p>Make a private github account.</p>
<p>Installation instructions will be a version of
<a class="reference external" href="http://practical-neuroimaging.github.io/installation.html">http://practical-neuroimaging.github.io/installation.html</a></p>
<p>Possible reading:</p>
<ul class="simple">
<li>Class introduction - e.g <a class="reference external" href="http://practical-neuroimaging.github.io/day0.html">http://practical-neuroimaging.github.io/day0.html</a></li>
<li>Numpy universe package summary - e.g.
<a class="reference external" href="http://nbviewer.ipython.org/urls/raw.github.com/practical-neuroimaging/pna-notebooks/master/python_libraries.ipynb">http://nbviewer.ipython.org/urls/raw.github.com/practical-neuroimaging/pna-notebooks/master/python_libraries.ipynb</a></li>
<li>Choosing a text editor - e.g.
<a class="reference external" href="http://practical-neuroimaging.github.io/choosing_editor.html">http://practical-neuroimaging.github.io/choosing_editor.html</a></li>
</ul>
<p>Day:</p>
<ul class="simple">
<li>Git clone practical neuroimaging notebooks;</li>
<li>Numpy / scipy / matplotlib / IPython notebook whirlwind introduction;</li>
<li>What is an image?</li>
<li>Loading a 3D image - from :
<a class="reference external" href="http://nbviewer.ipython.org/urls/raw.github.com/practical-neuroimaging/pna-notebooks/master/loading_images.ipynb">http://nbviewer.ipython.org/urls/raw.github.com/practical-neuroimaging/pna-notebooks/master/loading_images.ipynb</a></li>
<li>Getting pixel data</li>
<li>Text editor intro</li>
</ul>
</div>
</div>
<div class="section" id="day-1-introducing-python">
<h3>Day 1 : introducing Python<a class="headerlink" href="#day-1-introducing-python" title="Permalink to this headline">¶</a></h3>
<div class="section" id="id1">
<h4>Prior<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h4>
<ul class="simple">
<li>Make sure your Python environment installed on day 0 still works</li>
<li>Finish first Python Module on Codecademy: <a class="reference external" href="http://www.codecademy.com/courses/introduction-to-python-6WeG3/0/1">http://www.codecademy.com/courses/introduction-to-python-6WeG3/0/1</a></li>
</ul>
<p>Day:</p>
<ul class="simple">
<li>variables, math</li>
<li>flow control (conditional statements, loops)</li>
<li>basic data structures (lists and dictionaries)</li>
<li>importing modules</li>
<li>reading and parsing text files (basic)</li>
</ul>
</div>
</div>
<div class="section" id="day-2-images-as-arrays-and-plotting">
<h3>Day 2: images as arrays and plotting<a class="headerlink" href="#day-2-images-as-arrays-and-plotting" title="Permalink to this headline">¶</a></h3>
<p>Reading:</p>
<ul class="simple">
<li>Numpy introduction TBA</li>
<li>Matplotlib introduction TBA</li>
<li>What is an image? e.g.
<a class="reference external" href="http://nbviewer.ipython.org/urls/raw.github.com/practical-neuroimaging/pna-notebooks/master/what_is_an_image.ipynb">http://nbviewer.ipython.org/urls/raw.github.com/practical-neuroimaging/pna-notebooks/master/what_is_an_image.ipynb</a></li>
</ul>
<p>Day:</p>
<ul class="simple">
<li>Loading a 4D image (e.g.
<a class="reference external" href="http://nbviewer.ipython.org/urls/raw.github.com/practical-neuroimaging/pna-notebooks/master/loading_images.ipynb">http://nbviewer.ipython.org/urls/raw.github.com/practical-neuroimaging/pna-notebooks/master/loading_images.ipynb</a>)</li>
<li>Mean, standard deviation, variance over time</li>
<li>Text editor competition.</li>
</ul>
</div>
<div class="section" id="day-3-diagnostics-version-control">
<h3>Day 3: diagnostics / version control<a class="headerlink" href="#day-3-diagnostics-version-control" title="Permalink to this headline">¶</a></h3>
<p>Reading:</p>
<ul class="simple">
<li>Workflow e.g.
<a class="reference external" href="http://nbviewer.ipython.org/urls/raw.github.com/practical-neuroimaging/pna-notebooks/master/workflow.ipynb">http://nbviewer.ipython.org/urls/raw.github.com/practical-neuroimaging/pna-notebooks/master/workflow.ipynb</a></li>
<li>Git basics: <a class="reference external" href="http://matthew-brett.github.io/pydagogue/curious_git.html">http://matthew-brett.github.io/pydagogue/curious_git.html</a></li>
</ul>
<p>Day:</p>
<ul class="simple">
<li>Refresher on Python modules and packages;</li>
<li>Transfer notebook code into text files;</li>
<li>Add to git;</li>
<li>Time series diagnostics;</li>
<li>Make an edit and commit and push;</li>
</ul>
</div>
<div class="section" id="day-4-first-statistics-version-control">
<h3>Day 4: first statistics / version control<a class="headerlink" href="#day-4-first-statistics-version-control" title="Permalink to this headline">¶</a></h3>
<p>Reading:</p>
<ul class="simple">
<li>Make a branch, edit and commit;</li>
<li>Merge;</li>
<li>Push;</li>
<li>Splitting FMRI time series by slicing;</li>
<li>Subtracting on blocks from off blocks;</li>
<li>Visualizing result.</li>
</ul>
</div>
<div class="section" id="day-5-convolution-and-correlation">
<h3>Day 5: convolution and correlation<a class="headerlink" href="#day-5-convolution-and-correlation" title="Permalink to this headline">¶</a></h3>
<p>Reading:</p>
<ul class="simple">
<li>Something on hemodynamic modeling;</li>
<li>Notebook on convolution - an edited version of
<a class="reference external" href="http://nbviewer.ipython.org/urls/raw.github.com/practical-neuroimaging/pna-notebooks/master/convolution.ipynb">http://nbviewer.ipython.org/urls/raw.github.com/practical-neuroimaging/pna-notebooks/master/convolution.ipynb</a></li>
</ul>
<p>Day:</p>
<ul class="simple">
<li>Creating the convolution kernel;</li>
<li>Extracting time series (slicing in 4th dimension);</li>
<li>Convolution the dumb way;</li>
<li>Convolution the scipy way;</li>
<li>Correlating the convolved time course with the data.</li>
</ul>
</div>
<div class="section" id="day-6-regression-and-the-general-linear-model">
<h3>Day 6: regression and the general linear model<a class="headerlink" href="#day-6-regression-and-the-general-linear-model" title="Permalink to this headline">¶</a></h3>
<p>Reading:</p>
<ul class="simple">
<li>Poline and Brett 2012 : <a class="reference external" href="http://matthew.dynevor.org/_downloads/does_glm_love.pdf">http://matthew.dynevor.org/_downloads/does_glm_love.pdf</a></li>
<li>Notebook on GLM / contrasts - at: <a class="reference external" href="http://perrin.dynevor.org/glm_intro.html">http://perrin.dynevor.org/glm_intro.html</a></li>
</ul>
<p>Day:</p>
<ul class="simple">
<li>Load time course;</li>
<li>rebuild convolved regressor;</li>
<li>set up matrices;</li>
<li>run estimation;</li>
<li>visualize result;</li>
<li>replicate subtraction analysis from previous day with dummy regressors;</li>
<li>visualize result;</li>
<li>(relationship of correlation and regression).</li>
</ul>
</div>
<div class="section" id="day-7-diagnostics-using-principal-component-analysis">
<h3>Day 7: diagnostics using principal component analysis<a class="headerlink" href="#day-7-diagnostics-using-principal-component-analysis" title="Permalink to this headline">¶</a></h3>
<p>This day is for us to practice working with matrices, and to get an idea of
the level of underlying variance in data.</p>
<p>Reading:</p>
<ul class="simple">
<li>Notebook on diagnostics:
<a class="reference external" href="http://nbviewer.ipython.org/urls/bitbucket.org/matthewbrett/talks/raw/master/processing_i/diagnostics.ipynb">http://nbviewer.ipython.org/urls/bitbucket.org/matthewbrett/talks/raw/master/processing_i/diagnostics.ipynb</a></li>
<li>A tutorial on principal component analysis: <a class="reference external" href="http://arxiv.org/abs/1404.1100">http://arxiv.org/abs/1404.1100</a></li>
</ul>
<p>Day:</p>
<ul class="simple">
<li>Get code from notebook;</li>
<li>Run PCA;</li>
<li>Fetch projection matrices, vectors and values;</li>
<li>Reconstruct data using reduced number of components.</li>
<li>Investigate and diagnose components;</li>
<li>Investigate correlation of vectors with data.</li>
</ul>
</div>
<div class="section" id="day-8-1d-interpolation-and-slice-timing">
<h3>Day 8: 1D interpolation and slice timing<a class="headerlink" href="#day-8-1d-interpolation-and-slice-timing" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li>Notebook on interpolation and slice timing e.g.
<a class="reference external" href="http://nbviewer.ipython.org/urls/bitbucket.org/matthewbrett/talks/raw/master/processing_i/slice_timing.ipynb">http://nbviewer.ipython.org/urls/bitbucket.org/matthewbrett/talks/raw/master/processing_i/slice_timing.ipynb</a></li>
</ul>
<p>Day:</p>
<ul class="simple">
<li>Convert notebook to Python module;</li>
<li>write code to do linear interpolation on example time series;</li>
<li>write tests;</li>
<li>use scipy interpolation code;</li>
<li>investigate splines.</li>
</ul>
</div>
<div class="section" id="day-9-optimization-2d-interpolation-and-registration">
<h3>Day 9: optimization, 2D interpolation and registration<a class="headerlink" href="#day-9-optimization-2d-interpolation-and-registration" title="Permalink to this headline">¶</a></h3>
<p>Reading:</p>
<ul class="simple">
<li>Notebook on optimization for registration:
<a class="reference external" href="http://nbviewer.ipython.org/urls/bitbucket.org/matthewbrett/talks/raw/master/processing_i/optimizing_space.ipynb">http://nbviewer.ipython.org/urls/bitbucket.org/matthewbrett/talks/raw/master/processing_i/optimizing_space.ipynb</a></li>
</ul>
<p>(Add subtracted image after registration).</p>
<p>Day:</p>
<ul class="simple">
<li>Convert optimization notebook to Python module;</li>
<li>Run;</li>
<li>Try different cost functions;</li>
<li>Try different optimization methods;</li>
<li>Local minima with a 180 degree rotation;</li>
<li>Investigate and run FSL motion correction.</li>
</ul>
</div>
<div class="section" id="day-10-coordinate-systems-and-cross-modality-registration">
<h3>Day 10: coordinate systems and cross-modality registration<a class="headerlink" href="#day-10-coordinate-systems-and-cross-modality-registration" title="Permalink to this headline">¶</a></h3>
<p>Reading:</p>
<ul class="simple">
<li>Tutorial on coordinate systems at:
<a class="reference external" href="http://nipy.org/nibabel/coordinate_systems.html">http://nipy.org/nibabel/coordinate_systems.html</a></li>
<li>Mutual information : e.g.
<a class="reference external" href="http://nbviewer.ipython.org/urls/bitbucket.org/matthewbrett/talks/raw/master/processing_i/mutual_information_example.ipynb">http://nbviewer.ipython.org/urls/bitbucket.org/matthewbrett/talks/raw/master/processing_i/mutual_information_example.ipynb</a></li>
</ul>
<p>Need to fix this up.</p>
<p>Day:</p>
<ul class="simple">
<li>Load EPI;</li>
<li>Load anatomical;</li>
<li>Reslicing using coordinate transforms;</li>
<li>Scipy ndimage and affine_transform;</li>
<li>FSL coregistration;</li>
<li>SPM coregistration.</li>
</ul>
</div>
<div class="section" id="day-11-registration-between-subjects">
<h3>Day 11: registration between subjects<a class="headerlink" href="#day-11-registration-between-subjects" title="Permalink to this headline">¶</a></h3>
<p>Reading:</p>
<ul class="simple">
<li>Localization paper at <a class="reference external" href="http://matthew.dynevor.org/_downloads/location.pdf">http://matthew.dynevor.org/_downloads/location.pdf</a></li>
<li>Tutorial on inter-subject registration (spatial normalization). Yet to be
written. Some material at:<ul>
<li><a class="reference external" href="http://nbviewer.ipython.org/urls/raw.github.com/practical-neuroimaging/pna-notebooks/master/ANTS_normalization.ipynb">http://nbviewer.ipython.org/urls/raw.github.com/practical-neuroimaging/pna-notebooks/master/ANTS_normalization.ipynb</a></li>
<li><a class="reference external" href="http://nbviewer.ipython.org/urls/raw.github.com/practical-neuroimaging/pna-notebooks/master/template_registration.ipynb">http://nbviewer.ipython.org/urls/raw.github.com/practical-neuroimaging/pna-notebooks/master/template_registration.ipynb</a></li>
<li><a class="reference external" href="http://nipy.org/dipy/examples_built/syn_registration_2d.html#example-syn-registration-2d">http://nipy.org/dipy/examples_built/syn_registration_2d.html#example-syn-registration-2d</a></li>
<li><a class="reference external" href="http://nipy.org/dipy/examples_built/syn_registration_2d.html#example-syn-registration-3d">http://nipy.org/dipy/examples_built/syn_registration_2d.html#example-syn-registration-3d</a></li>
</ul>
</li>
</ul>
<p>Day:</p>
<ul class="simple">
<li>Affine registration using scipy;</li>
<li>Affine registration using FSL;</li>
<li>Warping in 2D using dipy regtools;</li>
<li>Diagnosing the warp using the deformation mesh;</li>
<li>Affine plus warping using FSL;</li>
<li>Thinking about what makes a good registration.</li>
</ul>
</div>
<div class="section" id="day-12-smoothing-and-modeling">
<h3>Day 12: smoothing and modeling<a class="headerlink" href="#day-12-smoothing-and-modeling" title="Permalink to this headline">¶</a></h3>
<p>Reading:</p>
<ul class="simple">
<li>Introduction to smoothing: <a class="reference external" href="http://perrin.dynevor.org/smoothing_intro.html">http://perrin.dynevor.org/smoothing_intro.html</a></li>
</ul>
<p>Day:</p>
<ul class="simple">
<li>Smoothing as convolution;</li>
<li>HRF regressor model on smoothed and unsmoothed data;</li>
<li>Different smoothing levels;</li>
<li>single voxel;</li>
<li>whole brain.</li>
</ul>
</div>
<div class="section" id="day-13-testing-hypotheses-with-t-and-f-contrasts">
<h3>Day 13: testing hypotheses with t and F contrasts<a class="headerlink" href="#day-13-testing-hypotheses-with-t-and-f-contrasts" title="Permalink to this headline">¶</a></h3>
<dl class="docutils">
<dt>Reading:</dt>
<dd><ul class="first last simple">
<li>Notebook on t / F - version of :
<a class="reference external" href="http://nbviewer.ipython.org/urls/raw.github.com/practical-neuroimaging/pna-notebooks/master/GLM_t_F.ipynb">http://nbviewer.ipython.org/urls/raw.github.com/practical-neuroimaging/pna-notebooks/master/GLM_t_F.ipynb</a></li>
</ul>
</dd>
</dl>
<p>Day:</p>
<ul class="simple">
<li>Block (on / off model) F contrasts;</li>
<li>Motion parameters as confounds;</li>
<li>t contrasts for motion;</li>
<li>F contrasts for motion;</li>
<li>FSL contrasts;</li>
<li>SPM contrasts.</li>
</ul>
</div>
<div class="section" id="day-14-random-effects-choosing-models">
<h3>Day 14: random effects, choosing models<a class="headerlink" href="#day-14-random-effects-choosing-models" title="Permalink to this headline">¶</a></h3>
<p>Reading:</p>
<ul class="simple">
<li>random versus fixed effect in neuroimaging litterature
<a class="reference external" href="ftp://193.62.66.20/spm/data/face_rfx/RFXabstract.pdf">ftp://193.62.66.20/spm/data/face_rfx/RFXabstract.pdf</a>
<a class="reference external" href="http://www.fil.ion.ucl.ac.uk/spm/doc/books/hbf2/pdfs/Ch12.pdf">http://www.fil.ion.ucl.ac.uk/spm/doc/books/hbf2/pdfs/Ch12.pdf</a></li>
<li>a simple tutorial on cross validation
<a class="reference external" href="http://www.autonlab.org/tutorials/overfit.html">http://www.autonlab.org/tutorials/overfit.html</a></li>
</ul>
<p>Day:</p>
<ul class="simple">
<li><dl class="first docutils">
<dt>Recall t and F tests: what are they doing exactly. Where does my variability come from?</dt>
<dd><ul class="first last">
<li>example within run (from previous course)</li>
<li>example random effect (between subjects).</li>
</ul>
</dd>
</dl>
</li>
<li>Wait a second: What is a model exactly ?</li>
<li>I am chosing a very wrong model: Consequences on the results of t/F tests.</li>
<li><dl class="first docutils">
<dt>How do I know my model is - is not very wrong? the good, the bad, the ugly (reverse order)</dt>
<dd><ul class="first last">
<li>the ugly: p-hacking. Let’s try it.</li>
<li>the bad: use R2.</li>
<li>the good: model validation</li>
</ul>
</dd>
</dl>
</li>
<li>Model validation: Principle. Example of “random effect” model testing the effect of “grumpiness”.</li>
</ul>
</div>
<div class="section" id="day-15-statistical-inference">
<h3>Day 15: statistical inference<a class="headerlink" href="#day-15-statistical-inference" title="Permalink to this headline">¶</a></h3>
<p>Reading:</p>
<ul class="simple">
<li>Bonferroni correction : e.g.
<a class="reference external" href="http://nbviewer.ipython.org/urls/raw.github.com/practical-neuroimaging/pna-notebooks/master/bonferonni_notes.ipynb">http://nbviewer.ipython.org/urls/raw.github.com/practical-neuroimaging/pna-notebooks/master/bonferonni_notes.ipynb</a></li>
<li>Random fields : e.g. <a class="reference external" href="http://perrin.dynevor.org/random_fields.html">http://perrin.dynevor.org/random_fields.html</a></li>
<li>FDR: <a class="reference external" href="http://nbviewer.ipython.org/github/practical-neuroimaging/pna-notebooks/blob/master/FDR.ipynb">http://nbviewer.ipython.org/github/practical-neuroimaging/pna-notebooks/blob/master/FDR.ipynb</a></li>
</ul>
<p>Day:</p>
<ul class="simple">
<li>Generate map of T</li>
<li>correct using Bonferroni;</li>
<li>correct using random fields;</li>
<li>correct using FDR;</li>
</ul>
</div>
<div class="section" id="possible-extra-days">
<h3>Possible extra days<a class="headerlink" href="#possible-extra-days" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li>Using machine learning tools with scikit-learn;</li>
<li>Introduction to diffusion imaging;</li>
<li>Introduction to DICOM;</li>
<li>Data visualization.</li>
</ul>
</div>
</div>
</div>
</div>
</div>
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<h3><a href="index.html">Table Of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">Practical neuroimaging, the sequel</a><ul>
<li><a class="reference internal" href="#who-is-this-course-for">Who is this course for?</a></li>
<li><a class="reference internal" href="#requirements">Requirements</a></li>
<li><a class="reference internal" href="#background">Background</a><ul>
<li><a class="reference internal" href="#practical-workflow">Practical workflow</a></li>
<li><a class="reference internal" href="#relevant-concepts">Relevant concepts</a></li>
<li><a class="reference internal" href="#fmri-analysis-steps">FMRI analysis steps</a></li>
</ul>
</li>
<li><a class="reference internal" href="#format-of-the-classes">Format of the classes</a></li>
<li><a class="reference internal" href="#general-reading">General reading</a></li>
<li><a class="reference internal" href="#text-editors">Text editors</a></li>
<li><a class="reference internal" href="#general-teaching-points">General teaching points</a></li>
<li><a class="reference internal" href="#schedule">Schedule</a><ul>
<li><a class="reference internal" href="#day-0-introduction-and-taster">Day 0 : introduction and taster</a><ul>
<li><a class="reference internal" href="#prior">Prior</a></li>
</ul>
</li>
<li><a class="reference internal" href="#day-1-introducing-python">Day 1 : introducing Python</a><ul>
<li><a class="reference internal" href="#id1">Prior</a></li>
</ul>
</li>
<li><a class="reference internal" href="#day-2-images-as-arrays-and-plotting">Day 2: images as arrays and plotting</a></li>
<li><a class="reference internal" href="#day-3-diagnostics-version-control">Day 3: diagnostics / version control</a></li>
<li><a class="reference internal" href="#day-4-first-statistics-version-control">Day 4: first statistics / version control</a></li>
<li><a class="reference internal" href="#day-5-convolution-and-correlation">Day 5: convolution and correlation</a></li>
<li><a class="reference internal" href="#day-6-regression-and-the-general-linear-model">Day 6: regression and the general linear model</a></li>
<li><a class="reference internal" href="#day-7-diagnostics-using-principal-component-analysis">Day 7: diagnostics using principal component analysis</a></li>
<li><a class="reference internal" href="#day-8-1d-interpolation-and-slice-timing">Day 8: 1D interpolation and slice timing</a></li>
<li><a class="reference internal" href="#day-9-optimization-2d-interpolation-and-registration">Day 9: optimization, 2D interpolation and registration</a></li>
<li><a class="reference internal" href="#day-10-coordinate-systems-and-cross-modality-registration">Day 10: coordinate systems and cross-modality registration</a></li>
<li><a class="reference internal" href="#day-11-registration-between-subjects">Day 11: registration between subjects</a></li>
<li><a class="reference internal" href="#day-12-smoothing-and-modeling">Day 12: smoothing and modeling</a></li>
<li><a class="reference internal" href="#day-13-testing-hypotheses-with-t-and-f-contrasts">Day 13: testing hypotheses with t and F contrasts</a></li>
<li><a class="reference internal" href="#day-14-random-effects-choosing-models">Day 14: random effects, choosing models</a></li>
<li><a class="reference internal" href="#day-15-statistical-inference">Day 15: statistical inference</a></li>
<li><a class="reference internal" href="#possible-extra-days">Possible extra days</a></li>
</ul>
</li>
</ul>
</li>
</ul>
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