Merge pull request #81 from FCP-INDI/master

jpellman · web-flow · commit e0bdac99edf5 · 2016-06-13T10:50:46.000-04:00
Merge master into dev.
diff --git a/.gitignore b/.gitignore
@@ -0,0 +1 @@
+*.swp
diff --git a/docs/user/_sources/.compute_config.txt.swp b/docs/user/_sources/.compute_config.txt.swp
diff --git a/docs/user/_sources/.index.txt.swp b/docs/user/_sources/.index.txt.swp
diff --git a/docs/user/_sources/cloud.txt b/docs/user/_sources/cloud.txt
@@ -230,7 +230,7 @@ By default, the cluster will have an EBS-backed root volume and, if available, a
 
 .. code-block:: bash
 
-   starcluster createvolume --shutdown-volume-host <volume_size_in_gigabytes> <region>
+   starcluster createvolume --shutdown-volume-host <volume_size_in_gigabytes> <region> -I t2.micro -i ami-6b211202
 
 Type ``starcluster listvolumes`` and get the `volume-id` for the volume that you just created.  Open up your Starcluster configuration file and add the following volume definition::
 
diff --git a/docs/user/_sources/nuisance.txt b/docs/user/_sources/nuisance.txt
@@ -1,6 +1,6 @@
 Nuisance Signal Regression
 --------------------------
-A key step in preparing fMRI data for statistical analysis is the removal of nusiance signals and noise. C-PAC provides a number of options for removing nuisance signals. These methods can be combined as desired by the user, and are described below.
+A key step in preparing fMRI data for statistical analysis is the removal of nusiance signals and noise. C-PAC provides a number of options for removing nuisance signals. These methods can be combined as desired by you, and are described below.
 
 White Matter / CSF Regression
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -42,7 +42,7 @@ Removes linear or quadratic trends in the timeseries. The linear trend is likely
 
 Temporal Filtering
 ^^^^^^^^^^^^^^^^^^
-The overall goal of temporal filtering is to increase the signal-to-noise ratio. Due to the relatively poor temporal resolution of fMRI, time series data contain little high-frequency noise. They do, however, often contain very slow frequency fluctuations that may be unrelated to the signal of interest. Slow changes in magnetic field strength may be responsible for part of the low-frequency signal observed in fMRI time series (Smith et al., 1999). Other factors contributing to noise in a time series are cardiac and respiratory effects, which will often show up as noise around ~0.15 and ~0.34 Hz, respectively (Wager et al., 2007).  The temporal filtering method implemented by C-PAC is relatively simple. Users specify a lower and upper bound for a band-pass filter, which then removes any information in frequencies outside the specified frequency band.
+The overall goal of temporal filtering is to increase the signal-to-noise ratio. Due to the relatively poor temporal resolution of fMRI, time series data contain little high-frequency noise. They do, however, often contain very slow frequency fluctuations that may be unrelated to the signal of interest. Slow changes in magnetic field strength may be responsible for part of the low-frequency signal observed in fMRI time series (Smith et al., 1999). Other factors contributing to noise in a time series are cardiac and respiratory effects, which will often show up as noise around ~0.15 and ~0.34 Hz, respectively (Wager et al., 2007).  The temporal filtering method implemented by C-PAC is relatively simple. You specify a lower and upper bound for a band-pass filter, which then removes any information in frequencies outside the specified frequency band.
 
 Recent work has revealed a portion of the low-frequency signal (0.01 to 0.1 Hz) to be the result of slow oscillations intrinsic to brain activity (Gee et al., 2011; Zuo et al., 2010; Schroeder and Lakatos, 2009). Utilizing measures such as Amplitude of Low Frequency Fluctuations (ALFF) and fractional ALFF, the power of these oscillations has been shown to differ both across subjects (Zang et al., 2007) and between conditions (Yan et al., 2009). Resting functional connectivity has been shown to be most prominent at these frequency bands (Cordes et al., 2001), and as such these fluctuations are commonly used to measure functional connectivity in the resting brain (Gee et al., 2010). 
 
diff --git a/docs/user/_sources/tse.txt b/docs/user/_sources/tse.txt
@@ -1,7 +1,7 @@
 =======================
  Timeseries Extraction
 =======================
-C-PAC lets users easily export BOLD timeseries in a number of different ways. This can be useful for those wishing to undertake advanced analysis not included in C-PAC, but still take advantage of its robust pre-processing features. For instructions on how to use these seeds within C-PAC
+C-PAC lets you easily export BOLD timeseries in a number of different ways. This can be useful for those wishing to undertake advanced analysis not included in C-PAC, but still take advantage of its robust pre-processing features. For instructions on how to use these seeds within C-PAC
 , please see the :doc:`Seed-based Correlation Analysis </sca>`.
 
 Define New Seeds
@@ -24,7 +24,7 @@ If multiple seeds are specified with the same resolution, they will be grouped i
 ROI Timeseries Extraction
 =========================
 
-ROI Timeseries Extraction allows you to export the timeseries for one or more regions of interest (ROIs). This is done by calculating the average timeseries across all voxels within an ROI. As such, C-PAC will output one timeseries for each ROI specified by the user.
+ROI Timeseries Extraction allows you to export the timeseries for one or more regions of interest (ROIs). This is done by calculating the average timeseries across all voxels within an ROI. As such, C-PAC will output one timeseries for each ROI specified by you.
 
 .. figure:: /_images/roi_timeseries.png
 
diff --git a/docs/user/cloud.html b/docs/user/cloud.html
@@ -216,7 +216,7 @@ <h3>Creating and Editing Your Configuration File<a class="headerlink" href="#cre
 <div class="section" id="attaching-persistent-storage-to-your-cluster">
 <h3>Attaching Persistent Storage to Your Cluster<a class="headerlink" href="#attaching-persistent-storage-to-your-cluster" title="Permalink to this headline">¶</a></h3>
 <p>By default, the cluster will have an EBS-backed root volume and, if available, an instance store volume mounted at <tt class="docutils literal"><span class="pre">/mnt</span></tt>.  Neither of these volumes are persistent and they will be destroyed when the cluster terminates. A shared directory mounted at <cite>/home</cite> on the head node can be used across nodes. If you need more storage than what is available on the head node or if you want to keep your data after the cluster is terminated, you will need to create a new volume that can be attached to all nodes in the cluster.  To do so, begin by creating an EBS-backed volume:</p>
-<div class="highlight-bash"><div class="highlight"><pre>starcluster createvolume --shutdown-volume-host &lt;volume_size_in_gigabytes&gt; &lt;region&gt;
+<div class="highlight-bash"><div class="highlight"><pre>starcluster createvolume --shutdown-volume-host &lt;volume_size_in_gigabytes&gt; &lt;region&gt; -I t2.micro -i ami-6b211202
 </pre></div>
 </div>
 <p>Type <tt class="docutils literal"><span class="pre">starcluster</span> <span class="pre">listvolumes</span></tt> and get the <cite>volume-id</cite> for the volume that you just created.  Open up your Starcluster configuration file and add the following volume definition:</p>
diff --git a/docs/user/nuisance.html b/docs/user/nuisance.html
@@ -55,7 +55,7 @@ <h3>Navigation</h3>
             
   <div class="section" id="nuisance-signal-regression">
 <h1>Nuisance Signal Regression<a class="headerlink" href="#nuisance-signal-regression" title="Permalink to this headline">¶</a></h1>
-<p>A key step in preparing fMRI data for statistical analysis is the removal of nusiance signals and noise. C-PAC provides a number of options for removing nuisance signals. These methods can be combined as desired by the user, and are described below.</p>
+<p>A key step in preparing fMRI data for statistical analysis is the removal of nusiance signals and noise. C-PAC provides a number of options for removing nuisance signals. These methods can be combined as desired by you, and are described below.</p>
 <div class="section" id="white-matter-csf-regression">
 <h2>White Matter / CSF Regression<a class="headerlink" href="#white-matter-csf-regression" title="Permalink to this headline">¶</a></h2>
 <p>Signal changes in the white matter (WM) and cerebrospinal fluid (CSF) primarily reflect non-neural fluctuations such as scanner instabilities, subject motion, and physiological artifacts (e.g. respiration and cardiac effects) (Dagli et al., 1999; Windischberger et al., 2002). These signals are largely independent from the BOLD signal fluctuations recorded in gray matter, and may introduce temporal coherences that lead to an overestimation of functional connectivity strength. Successful estimation and correction for this non-neural noise allows for the exclusion such confounding effects.</p>
@@ -93,7 +93,7 @@ <h2>Linear and Quadratic Detrending<a class="headerlink" href="#linear-and-quadr
 </div>
 <div class="section" id="temporal-filtering">
 <h2>Temporal Filtering<a class="headerlink" href="#temporal-filtering" title="Permalink to this headline">¶</a></h2>
-<p>The overall goal of temporal filtering is to increase the signal-to-noise ratio. Due to the relatively poor temporal resolution of fMRI, time series data contain little high-frequency noise. They do, however, often contain very slow frequency fluctuations that may be unrelated to the signal of interest. Slow changes in magnetic field strength may be responsible for part of the low-frequency signal observed in fMRI time series (Smith et al., 1999). Other factors contributing to noise in a time series are cardiac and respiratory effects, which will often show up as noise around ~0.15 and ~0.34 Hz, respectively (Wager et al., 2007).  The temporal filtering method implemented by C-PAC is relatively simple. Users specify a lower and upper bound for a band-pass filter, which then removes any information in frequencies outside the specified frequency band.</p>
+<p>The overall goal of temporal filtering is to increase the signal-to-noise ratio. Due to the relatively poor temporal resolution of fMRI, time series data contain little high-frequency noise. They do, however, often contain very slow frequency fluctuations that may be unrelated to the signal of interest. Slow changes in magnetic field strength may be responsible for part of the low-frequency signal observed in fMRI time series (Smith et al., 1999). Other factors contributing to noise in a time series are cardiac and respiratory effects, which will often show up as noise around ~0.15 and ~0.34 Hz, respectively (Wager et al., 2007).  The temporal filtering method implemented by C-PAC is relatively simple. You specify a lower and upper bound for a band-pass filter, which then removes any information in frequencies outside the specified frequency band.</p>
 <p>Recent work has revealed a portion of the low-frequency signal (0.01 to 0.1 Hz) to be the result of slow oscillations intrinsic to brain activity (Gee et al., 2011; Zuo et al., 2010; Schroeder and Lakatos, 2009). Utilizing measures such as Amplitude of Low Frequency Fluctuations (ALFF) and fractional ALFF, the power of these oscillations has been shown to differ both across subjects (Zang et al., 2007) and between conditions (Yan et al., 2009). Resting functional connectivity has been shown to be most prominent at these frequency bands (Cordes et al., 2001), and as such these fluctuations are commonly used to measure functional connectivity in the resting brain (Gee et al., 2010).</p>
 <p>As these low-frequency oscillations are likely of interest to researchers, it is important to take this knowledge into account when deciding on what temporal filtering settings to use. As a general rule, it is safe to filter frequencies below 0.0083 Hz (Ashby, 2011).</p>
 <p>Additionally, there is some evidence (Davey et al., 2012) that temporal filtering may induce correlation in resting fMRI data, breaking the assumption of temporal sample independence and potentially invalidating the results of connectivity analysis. This should be taken into account when running temporal filtering on data on which you will later run connectivity analysis.</p>
diff --git a/docs/user/searchindex.js b/docs/user/searchindex.js
diff --git a/docs/user/tse.html b/docs/user/tse.html
@@ -55,7 +55,7 @@ <h3>Navigation</h3>
             
   <div class="section" id="timeseries-extraction">
 <h1>Timeseries Extraction<a class="headerlink" href="#timeseries-extraction" title="Permalink to this headline">¶</a></h1>
-<p>C-PAC lets users easily export BOLD timeseries in a number of different ways. This can be useful for those wishing to undertake advanced analysis not included in C-PAC, but still take advantage of its robust pre-processing features. For instructions on how to use these seeds within C-PAC
+<p>C-PAC lets you easily export BOLD timeseries in a number of different ways. This can be useful for those wishing to undertake advanced analysis not included in C-PAC, but still take advantage of its robust pre-processing features. For instructions on how to use these seeds within C-PAC
 , please see the <a class="reference internal" href="sca.html"><em>Seed-based Correlation Analysis</em></a>.</p>
 <div class="section" id="define-new-seeds">
 <h2>Define New Seeds<a class="headerlink" href="#define-new-seeds" title="Permalink to this headline">¶</a></h2>
@@ -75,7 +75,7 @@ <h2>Define New Seeds<a class="headerlink" href="#define-new-seeds" title="Permal
 </div>
 <div class="section" id="roi-timeseries-extraction">
 <h2>ROI Timeseries Extraction<a class="headerlink" href="#roi-timeseries-extraction" title="Permalink to this headline">¶</a></h2>
-<p>ROI Timeseries Extraction allows you to export the timeseries for one or more regions of interest (ROIs). This is done by calculating the average timeseries across all voxels within an ROI. As such, C-PAC will output one timeseries for each ROI specified by the user.</p>
+<p>ROI Timeseries Extraction allows you to export the timeseries for one or more regions of interest (ROIs). This is done by calculating the average timeseries across all voxels within an ROI. As such, C-PAC will output one timeseries for each ROI specified by you.</p>
 <div class="figure">
 <img alt="_images/roi_timeseries.png" src="_images/roi_timeseries.png" />
 </div>
