Merge pull request #75 from FCP-INDI/dev0392

Fixed bullet list on index

docs/user/_sources/index.txt

@@ -8,8 +8,9 @@ Welcome to C-PAC's Documentation!
 Once a distant goal, discovery science for the human connectome is now a reality. Researchers who previously struggled to obtain neuroimaging data from 20-30 participants are now exploring the functional connectome using data acquired from thousands of participants, made publicly available through the `1000 Functional Connectomes Project and the International Neuroimaging Data-sharing Initiative (INDI) <http://fcon_1000.projects.nitrc.org/>`_. However, in addition to access to data, scientists need access to tools that will facilitate data exploration. Such tools are particularly important for those who are inexperienced with the nuances of fMRI image analysis, or those who lack the programming support necessary for handling and analyzing large-scale datasets.
 
 The Configurable Pipeline for the Analysis of Connectomes (C-PAC) is a configurable, open-source, Nipype-based, automated processing pipeline for resting state functional MRI (R-fMRI) data, for use by both novice and expert users. C-PAC was designed to bring the power, flexibility and elegance of the `Nipype platform <http://nipy.sourceforge.net/nipype/>`_ to users in a plug and play fashion—without requiring the ability to program. Using an easy to read, text-editable configuration file or a graphical user interface, C-PAC users can rapidly orchestrate automated R-fMRI processing procedures, including: 
+
 * standard quality assurance measurements
-* standard image :doc:`preprocessing </preproc>` based upon user specified preferences
+* standard image preprocessing  based upon user specified preferences
 * generation of functional connectivity maps (e.g., :doc:`seed-based correlation analyses </sca>`)
 * customizable extraction of time-series data
 * generation of graphical representations of the connectomes at various scales (e.g., voxel, parcellation unit)

docs/user/index.html

@@ -50,13 +50,15 @@ <h3>Navigation</h3>
   <div class="section" id="welcome-to-c-pac-s-documentation">
 <h1>Welcome to C-PAC&#8217;s Documentation!<a class="headerlink" href="#welcome-to-c-pac-s-documentation" title="Permalink to this headline">¶</a></h1>
 <p>Once a distant goal, discovery science for the human connectome is now a reality. Researchers who previously struggled to obtain neuroimaging data from 20-30 participants are now exploring the functional connectome using data acquired from thousands of participants, made publicly available through the <a class="reference external" href="http://fcon_1000.projects.nitrc.org/">1000 Functional Connectomes Project and the International Neuroimaging Data-sharing Initiative (INDI)</a>. However, in addition to access to data, scientists need access to tools that will facilitate data exploration. Such tools are particularly important for those who are inexperienced with the nuances of fMRI image analysis, or those who lack the programming support necessary for handling and analyzing large-scale datasets.</p>
-<p>The Configurable Pipeline for the Analysis of Connectomes (C-PAC) is a configurable, open-source, Nipype-based, automated processing pipeline for resting state functional MRI (R-fMRI) data, for use by both novice and expert users. C-PAC was designed to bring the power, flexibility and elegance of the <a class="reference external" href="http://nipy.sourceforge.net/nipype/">Nipype platform</a> to users in a plug and play fashion—without requiring the ability to program. Using an easy to read, text-editable configuration file or a graphical user interface, C-PAC users can rapidly orchestrate automated R-fMRI processing procedures, including:
-* standard quality assurance measurements
-* standard image <tt class="xref doc docutils literal"><span class="pre">preprocessing</span></tt> based upon user specified preferences
-* generation of functional connectivity maps (e.g., <a class="reference internal" href="sca.html"><em>seed-based correlation analyses</em></a>)
-* customizable extraction of time-series data
-* generation of graphical representations of the connectomes at various scales (e.g., voxel, parcellation unit)
-* generation of local R-fMRI measures (e.g., <a class="reference internal" href="reho.html"><em>regional homogeneity</em></a>, <a class="reference internal" href="vmhc.html"><em>voxel-matched homotopic connectivity</em></a>, <a class="reference internal" href="alff.html"><em>frequency amplitude measures</em></a>)</p>
+<p>The Configurable Pipeline for the Analysis of Connectomes (C-PAC) is a configurable, open-source, Nipype-based, automated processing pipeline for resting state functional MRI (R-fMRI) data, for use by both novice and expert users. C-PAC was designed to bring the power, flexibility and elegance of the <a class="reference external" href="http://nipy.sourceforge.net/nipype/">Nipype platform</a> to users in a plug and play fashion—without requiring the ability to program. Using an easy to read, text-editable configuration file or a graphical user interface, C-PAC users can rapidly orchestrate automated R-fMRI processing procedures, including:</p>
+<ul class="simple">
+<li>standard quality assurance measurements</li>
+<li>standard image preprocessing  based upon user specified preferences</li>
+<li>generation of functional connectivity maps (e.g., <a class="reference internal" href="sca.html"><em>seed-based correlation analyses</em></a>)</li>
+<li>customizable extraction of time-series data</li>
+<li>generation of graphical representations of the connectomes at various scales (e.g., voxel, parcellation unit)</li>
+<li>generation of local R-fMRI measures (e.g., <a class="reference internal" href="reho.html"><em>regional homogeneity</em></a>, <a class="reference internal" href="vmhc.html"><em>voxel-matched homotopic connectivity</em></a>, <a class="reference internal" href="alff.html"><em>frequency amplitude measures</em></a>)</li>
+</ul>
 <p>Importantly, C-PAC makes it possible to use a single configuration file to launch a factorial number of pipelines differing with respect to specific processing steps (e.g., spatial/temporal filter settings, global correction strategies, motion correction strategies, group analysis models). Additional noteworthy features include the ability to easily:</p>
 <ul class="simple">
 <li>customize C-PAC to handle any systematic directory organization</li>