📝 Ndmg connectome optional

Author: shnizzedy

docs/_sources/references/style.py

@@ -138,3 +138,6 @@ def get_misc_template(self, e):
             sentence[optional_field('note')],
         ]
         return template
+
+    def get_techreport_template(self, e):
+        return self.get_article_template(e)

docs/_sources/references/tse.bib

@@ -10,6 +10,27 @@ @article{Abra14
   abstract = {Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.}
 }
 
+@techreport{Kiar17,
+  type = {Preprint},
+  title = {A {{High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability}}},
+  author = {Kiar, Gregory and Bridgeford, Eric W. and Gray Roncal, William R. and {Consortium for Reliability and Reproducibility (CoRR)} and Chandrashekhar, Vikram and Mhembere, Disa and Ryman, Sephira and Zuo, Xi-Nian and Margulies, Daniel S. and Craddock, R. Cameron and Priebe, Carey E. and Jung, Rex and Calhoun, Vince D. and Caffo, Brian and Burns, Randal and Milham, Michael P. and Vogelstein, Joshua T.},
+  year = {2017},
+  month = sep,
+  doi = {10.1101/188706},
+  abstract = {Modern scientific discovery depends on collecting large heterogeneous datasets with many sources of variability, and applying domain-specific pipelines from which one can draw insight or clinical utility. For example, macroscale connectomics studies require complex pipelines to process raw functional or diffusion data and estimate connectomes. Individual studies tend to customize pipelines to their needs, raising concerns about their reproducibility, which add to a longer list of factors that may differ across studies and result in failures to replicate (including sampling, experimental design, and data acquisition protocols). Mitigating these issues requires multi-study datasets and the development of pipelines that can be applied across them. We developed NeuroData's MRI to Graphs (NDMG) pipeline using several functional and diffusion studies, including the Consortium for Reliability and Reproducability, to estimate connectomes. Without any manual intervention or parameter tuning, NDMG ran on 25 different studies ({$\approx$}6,000 scans) from 19 sites, with each scan resulting in a biologically plausible connectome (as assessed by multiple quality assurance metrics at each processing stage). For each study, the connectomes from NDMG are more similar within than across individuals, indicating that NDMG is preserving biological variability. Moreover, the connectomes exhibit near perfect consistency for certain connectional properties across every scan, individual, study, site and modality; these include stronger ipsilateral than contralateral connections and stronger homotopic than heterotopic connections. Yet, the magnitude of the differences varied across individuals and studies\textemdash much more so when pooling data across sites, even after controlling for study, site, and basic demographic variables (i.e., age, sex, and ethnicity). This indicates that other experimental variables (possibly those not measured or reported) are contributing to this variability, which if not accounted for can limit the value of aggregate datasets, as well as expectations regarding the accuracy of findings and likelihood of replication. We therefore provide a set of principles to guide the development of pipelines capable of pooling data across studies while maintaining biological variability and minimizing measurement error. This open science approach provides us with an opportunity to understand and eventually mitigate spurious results for both past and future studies.},
+  langid = {english}
+}
+
+@misc{Neur18,
+  title = {{{ndmg}}: {{NeuroData}}'s {{MR Graphs}} Package},
+  author = {{NeuroData}},
+  year = {2018},
+  month = nov,
+  url = {https://github.com/neurodata/m2g/tree/fa305cf2},
+  copyright = {apache-2.0},
+  howpublished = {NeuroData}
+}
+
 @incollection{nile21,
   title = {3.1.1.2. {{Extracting}} Signals on a Parcellation},
   booktitle = {Nilearn: Statistics for Neuroimaging in {{Python}}: User Guide},

docs/_sources/user/tse.rst

@@ -24,7 +24,7 @@ Configuring ROI Time Series Extraction
 
 #. **Realignment - [ROI to func, func to ROI]:** Choose functional time-series and ROI realignment method. 'ROI to func' will realign the atlas/ROI to functional space (fast). 'func to ROI' will realign the functional time series to the atlas/ROI space. NOTE: in rare cases, realigning the ROI to the functional space may result in small misalignments for very small ROIs - please double check your data if you see issues.
 
-#. **Connectivity Matrix:** A connectivity matrix can be generated via nilearn :cite:`cite-Abra14,cite-nile21,cite-nile21a` or AFNI for the ``Avg`` timeseries output.
+#. **Connectivity Matrix:** A connectivity matrix can be generated via nilearn :cite:`cite-Abra14,cite-nile21,cite-nile21a`, ndmg :cite:`cite-Kiar17,cite-Neur18` or AFNI for the ``Avg`` timeseries output.
 
 .. include:: /user/pipelines/without_gui.rst