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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
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<title>Course on Practical Neuroimaging in Python — Practical neuroimaging analysis</title>
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<div class="section" id="example-datasets">
<h1>Example datasets<a class="headerlink" href="#example-datasets" title="Permalink to this headline">¶</a></h1>
<p>We will be using data from two <a class="reference external" href="https://openfmri.org/">OpenFMRI</a> projects – <code class="docutils literal"><span class="pre">ds114</span></code> and
<code class="docutils literal"><span class="pre">ds107</span></code>. Data from both projects are covered by a Public Domain Dedication
and License version 1.0 (<a class="reference external" href="http://opendatacommons.org/licenses/pddl/1.0/">PDDL 1.0</a>).</p>
<div class="section" id="dataset-ds114">
<h2>Dataset ds114<a class="headerlink" href="#dataset-ds114" title="Permalink to this headline">¶</a></h2>
<p>We make most use of some files from the <a class="reference external" href="https://openfmri.org/dataset/ds000114">ds114 dataset</a>.</p>
<p>The cited article for this study is:</p>
<p><a class="reference external" href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3641991">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3641991</a></p>
<p>There are more details of the analysis in:</p>
<p><a class="reference external" href="http://www.ncbi.nlm.nih.gov/pubmed/23153967">http://www.ncbi.nlm.nih.gov/pubmed/23153967</a></p>
<p>In particular, we will be using the data from the covert verb generation task.
Here is the description from the second (NeuroImage) paper linked above:</p>
<blockquote>
<div>Subjects were asked to think of a verb complementing a noun presented to
them visually. The following instructions were used: “When a word appears
it will be a noun. Think of what you can do with it and then imagine
saying ‘With that I can …’ or ‘That I can …’ ”. A block design with 30 s
activation and 30 s rest blocks was employed, with eight trials used for
training. During the activation blocks, ten nouns were presented for 1 s
each followed by a fixation cross during which subject had to generate the
response. The nouns were chosen at random from a set of 70 nouns (mean
lexical frequency: 0.000087, min: 0.000005, max: 0.000392, std: 0.000092).
Rest blocks had an analogous structure but with each word replaced by
scrambled visual patterns generated by scrambling the phase of the
‘picture’ of each word, i.e. the control patterns were matched in the
amplitude spectrum. Seven activation/rest blocks were presented for a
total scan time of 7 min 12.5 s.</div></blockquote>
<p>There are more acquisition parameters listed at
<a class="reference external" href="http://gigascience.biomedcentral.com/articles/10.1186/2047-217X-2-6#Tab1">http://gigascience.biomedcentral.com/articles/10.1186/2047-217X-2-6#Tab1</a></p>
<p>The analysis scripts are available at:
<a class="reference external" href="https://github.com/chrisfilo/2010-Reliability-Study">https://github.com/chrisfilo/2010-Reliability-Study</a></p>
<p>From the links above, for the covert verb generation task:</p>
<ul class="simple">
<li>GE Signa 1.5T scanner</li>
<li><dl class="first docutils">
<dt>EPI (functional images)</dt>
<dd><ul class="first last">
<li>TR = 2.5 s</li>
<li>TE = 50 ms</li>
<li>FOV = 256 x 256 mm</li>
<li>matrix = 64 × 64</li>
<li>4 x 4 x 4 mm voxel size</li>
<li>0 slice gap</li>
<li>173 volumes per run</li>
<li>‘Interleaved’ slice timing acquisition order</li>
</ul>
</dd>
</dl>
</li>
<li><dl class="first docutils">
<dt>3D Inversion Recovery Prepared (anatomical images)</dt>
<dd><ul class="first last">
<li>TR = 10 s</li>
<li>TE = 4 s</li>
<li>1 x 1 x 1.3 mm voxel size</li>
<li>0 slice gap</li>
</ul>
</dd>
</dl>
</li>
</ul>
<p>The original analysis assumed ascending interleaved slice acquisition order -
see <a class="reference external" href="https://github.com/chrisfilo/2010-Reliability-Study/blob/master/src/helper_functions.py#L35">the analysis script</a>.</p>
<p>The original analysis dropped 4 dummy scans from the functional runs before
continuing the analysis.</p>
<p>The onset times in the OpenFMRI model specification files almost certainly
take time = 0 to be the beginning of the first dummy scan.</p>
</div>
<div class="section" id="dataset-ds107">
<h2>Dataset ds107<a class="headerlink" href="#dataset-ds107" title="Permalink to this headline">¶</a></h2>
<p>We will also be using data from the <a class="reference external" href="https://openfmri.org/dataset/ds000107">ds107 dataset</a>.</p>
<p>Here is the article describing dataset and analysis:</p>
<p><a class="reference external" href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2686646/">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2686646/</a></p>
<p>From the methods section of that article, we find these bits of information:</p>
<ul class="simple">
<li>Siemens 1.5 T MR scanner</li>
<li><dl class="first docutils">
<dt>EPI (functional images)</dt>
<dd><ul class="first last">
<li>TR = 3000 ms</li>
<li>TE = 50 ms</li>
<li>FOV = 192 × 192</li>
<li>matrix = 64 × 64</li>
<li>“notional resolution” 3 × 3 × 3 mm</li>
<li>164 volumes per run</li>
</ul>
</dd>
</dl>
</li>
<li><dl class="first docutils">
<dt>T1 FLASH (anatomical images)</dt>
<dd><ul class="first last">
<li>TR = 12 ms</li>
<li>TE = 5.6 ms</li>
<li>1 mm3 resolution</li>
</ul>
</dd>
</dl>
</li>
</ul>
<div class="section" id="missing-information">
<h3>Missing information<a class="headerlink" href="#missing-information" title="Permalink to this headline">¶</a></h3>
<p>At least - I couldn’t find this information at a quick scan - please correct
me if I’m wrong:</p>
<ul class="simple">
<li>Slice order (interleaved? sequential?)</li>
<li>Volume gap in time (any gap between the last slice from volume N and the
first slice of volume N+1?)</li>
<li>Slice width (was there a gap between the slices)</li>
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<li><a class="reference internal" href="#">Example datasets</a><ul>
<li><a class="reference internal" href="#dataset-ds114">Dataset ds114</a></li>
<li><a class="reference internal" href="#dataset-ds107">Dataset ds107</a><ul>
<li><a class="reference internal" href="#missing-information">Missing information</a></li>
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