Added ref for rodent fMRI data quality issues.

Author: Gab-D-G

docs/_static/refs.bib

@@ -649,6 +649,56 @@ @ARTICLE{Muschelli2014-vi
               Specificity;RABIES documentation"
 }
 
+@ARTICLE{Grandjean2020-fa,
+  title    = "Common functional networks in the mouse brain revealed by
+              multi-centre resting-state {fMRI} analysis",
+  author   = "Grandjean, Joanes and Canella, Carola and Anckaerts, Cynthia and
+              Ayranc{\i}, G{\"u}lebru and Bougacha, Salma and Bienert, Thomas
+              and Buehlmann, David and Coletta, Ludovico and Gallino, Daniel
+              and Gass, Natalia and Garin, Cl{\'e}ment M and Nadkarni, Nachiket
+              Abhay and H{\"u}bner, Neele S and Karatas, Meltem and Komaki,
+              Yuji and Kreitz, Silke and Mandino, Francesca and Mechling, Anna
+              E and Sato, Chika and Sauer, Katja and Shah, Disha and Strobelt,
+              Sandra and Takata, Norio and Wank, Isabel and Wu, Tong and
+              Yahata, Noriaki and Yeow, Ling Yun and Yee, Yohan and Aoki, Ichio
+              and Chakravarty, M Mallar and Chang, Wei-Tang and Dhenain, Marc
+              and von Elverfeldt, Dominik and Harsan, Laura-Adela and Hess,
+              Andreas and Jiang, Tianzi and Keliris, Georgios A and Lerch,
+              Jason P and Meyer-Lindenberg, Andreas and Okano, Hideyuki and
+              Rudin, Markus and Sartorius, Alexander and Van der Linden,
+              Annemie and Verhoye, Marleen and Weber-Fahr, Wolfgang and
+              Wenderoth, Nicole and Zerbi, Valerio and Gozzi, Alessandro",
+  abstract = "Preclinical applications of resting-state functional magnetic
+              resonance imaging (rsfMRI) offer the possibility to
+              non-invasively probe whole-brain network dynamics and to
+              investigate the determinants of altered network signatures
+              observed in human studies. Mouse rsfMRI has been increasingly
+              adopted by numerous laboratories worldwide. Here we describe a
+              multi-centre comparison of 17 mouse rsfMRI datasets via a common
+              image processing and analysis pipeline. Despite prominent
+              cross-laboratory differences in equipment and imaging procedures,
+              we report the reproducible identification of several large-scale
+              resting-state networks (RSN), including a mouse default-mode
+              network, in the majority of datasets. A combination of factors
+              was associated with enhanced reproducibility in functional
+              connectivity parameter estimation, including animal handling
+              procedures and equipment performance. RSN spatial specificity was
+              enhanced in datasets acquired at higher field strength, with
+              cryoprobes, in ventilated animals, and under
+              medetomidine-isoflurane combination sedation. Our work describes
+              a set of representative RSNs in the mouse brain and highlights
+              key experimental parameters that can critically guide the design
+              and analysis of future rodent rsfMRI investigations.",
+  journal  = "Neuroimage",
+  volume   =  205,
+  pages    = "116278",
+  month    =  jan,
+  year     =  2020,
+  keywords = "Connectome; Default-mode network; Functional connectivity; ICA;
+              Seed-based;RABIES documentation",
+  language = "en"
+}
+
 @ARTICLE{Nickerson2017-gq,
   title     = "Using dual regression to investigate network shape and amplitude
                in functional connectivity analyses",

docs/analysis_QC.md

@@ -12,7 +12,7 @@ nested_docs/group_stats.md
 nested_docs/optim_CR.md
 ```
 
-Data quality can have serious impacts on analysis outcomes, leading to false findings. Rodent imaging can suffer from spurious effects on connectivity measures if potential confounds are not well accounted for, or acquisition factors, such as anesthesia levels, can influence network activity (refs to Jo Multisite + RABIES preprint). To support interpretability, troubleshooting and reproducible research, RABIES includes a set of reports for conducting data quality assessment in individual scans and conducting quality control prior to network analysis at the group level. The reports are designed most specifically to evaluate the detectability of canonical brain networks and the impact of potential confounds (motion, physiological instabilities, and more). 
+Data quality can have serious impacts on analysis outcomes, leading to false findings. Rodent imaging can suffer from spurious effects on connectivity measures if potential confounds are not well accounted for, or acquisition factors, such as anesthesia levels, can influence network activity {cite}`Desrosiers-Gregoire2023-jm,Grandjean2020-fa`. To support interpretability, troubleshooting and reproducible research, RABIES includes a set of reports for conducting data quality assessment in individual scans and conducting quality control prior to network analysis at the group level. The reports are designed most specifically to evaluate the detectability of canonical brain networks and the impact of potential confounds (motion, physiological instabilities, and more). 
 
 This page describes how to generate the reports, our guidelines for conducting quality network of network analysis, and how to include those reports in a publication.