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@article{power2012spurious,
title={Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion},
author={Power, Jonathan D and Barnes, Kelly A and Snyder, Abraham Z and Schlaggar, Bradley L and Petersen, Steven E},
journal={Neuroimage},
volume={59},
number={3},
pages={2142--2154},
year={2012},
publisher={Elsevier}
}
@article{behzadi2007component,
title={A component based noise correction method (CompCor) for BOLD and perfusion based fMRI},
author={Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T},
journal={Neuroimage},
volume={37},
number={1},
pages={90--101},
year={2007},
publisher={Elsevier}
}
@article{tustison_n4itk_2010,
title = {N4ITK: Improved N3 Bias Correction},
volume = {29},
issn = {0278-0062},
url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3071855/},
doi = {10.1109/TMI.2010.2046908},
shorttitle = {N4ITK},
abstract = {A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction. Given the superb performance of N3 and its public availability, it has been the subject of several evaluation studies. These studies have demonstrated the importance of certain parameters associated with the B-spline least-squares fitting. We propose the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field correction over the original N3 algorithm. Similar to the N3 algorithm, we also make the source code, testing, and technical documentation of our contribution, which we denote as “N4ITK,” available to the public through the Insight Toolkit of the National Institutes of Health. Performance assessment is demonstrated using simulated data from the publicly available Brainweb database, hyperpolarized 3 He lung image data, and 9.4T postmortem hippocampus data.},
pages = {1310--1320},
number = {6},
journaltitle = {{IEEE} transactions on medical imaging},
shortjournal = {{IEEE} Trans Med Imaging},
author = {Tustison, Nicholas J. and Avants, Brian B. and Cook, Philip A. and Zheng, Yuanjie and Egan, Alexander and Yushkevich, Paul A. and Gee, James C.},
urldate = {2015-08-05},
date = {2010-06},
pmcid = {PMC3071855},
note = {00215 {PMID}: 20378467},
file = {PubMed Central Full Text PDF:/Users/johnmuschelli/Library/Application Support/Zotero/Profiles/9yinbn16.default/zotero/storage/Z6MNT36E/Tustison et al. - 2010 - N4ITK Improved N3 Bias Correction.pdf:application/pdf}
}
@article{avants_symmetric_2008,
title = {Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain},
volume = {12},
issn = {1361-8415},
url = {http://www.sciencedirect.com/science/article/pii/S1361841507000606},
doi = {10.1016/j.media.2007.06.004},
series = {Special Issue on The Third International Workshop on Biomedical Image Registration - {WBIR} 2006},
shorttitle = {Symmetric diffeomorphic image registration with cross-correlation},
abstract = {One of the most challenging problems in modern neuroimaging is detailed characterization of neurodegeneration. Quantifying spatial and longitudinal atrophy patterns is an important component of this process. These spatiotemporal signals will aid in discriminating between related diseases, such as frontotemporal dementia ({FTD}) and Alzheimer's disease ({AD}), which manifest themselves in the same at-risk population. Here, we develop a novel symmetric image normalization method ({SyN}) for maximizing the cross-correlation within the space of diffeomorphic maps and provide the Euler-Lagrange equations necessary for this optimization. We then turn to a careful evaluation of our method. Our evaluation uses gold standard, human cortical segmentation to contrast {SyN}'s performance with a related elastic method and with the standard {ITK} implementation of Thirion's Demons algorithm. The new method compares favorably with both approaches, in particular when the distance between the template brain and the target brain is large. We then report the correlation of volumes gained by algorithmic cortical labelings of {FTD} and control subjects with those gained by the manual rater. This comparison shows that, of the three methods tested, {SyN}'s volume measurements are the most strongly correlated with volume measurements gained by expert labeling. This study indicates that {SyN}, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric {MRI} of patients and at-risk elderly individuals.},
pages = {26--41},
number = {1},
journaltitle = {Medical Image Analysis},
shortjournal = {Medical Image Analysis},
author = {Avants, B. B. and Epstein, C. L. and Grossman, M. and Gee, J. C.},
urldate = {2014-10-28},
date = {2008-02},
note = {00548},
keywords = {Cross-correlation, Deformable image registration, dementia, Diffeomorphic, Human cortex, Morphometry},
file = {ScienceDirect Full Text PDF:/Users/johnmuschelli/Library/Application Support/Zotero/Profiles/9yinbn16.default/zotero/storage/XHBHNS3I/Avants et al. - 2008 - Symmetric diffeomorphic image registration with cr.pdf:application/pdf;ScienceDirect Snapshot:/Users/johnmuschelli/Library/Application Support/Zotero/Profiles/9yinbn16.default/zotero/storage/WNZHK6ZE/S1361841507000606.html:text/html}
}
@article{oishi2009atlas,
title={Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and Alzheimer's disease participants},
author={Oishi, Kenichi and Faria, Andreia and Jiang, Hangyi and Li, Xin and Akhter, Kazi and Zhang, Jiangyang and Hsu, John T and Miller, Michael I and van Zijl, Peter CM and Albert, Marilyn and others},
journal={Neuroimage},
volume={46},
number={2},
pages={486--499},
year={2009},
publisher={Elsevier}
}
@Article{Oishi_Faria_Mori2010,
author = {Oishi, Kenichi and Faria, Andreia and Mori, Susumu},
title = {JHU-MNI-ss Atlas},
year = {2010},
month = {05},
journal = {https://www.slicer.org/publications/item/view/1883},
Institution = {Johns Hopkins University School of Medicine, Department of Radiology, Center for Brain Imaging Science}
}
@article{sweeney2013automatic,
title={Automatic lesion incidence estimation and detection in multiple sclerosis using multisequence longitudinal MRI},
author={Sweeney, EM and Shinohara, RT and Shea, CD and Reich, DS and Crainiceanu, CM},
journal={American Journal of Neuroradiology},
volume={34},
number={1},
pages={68--73},
year={2013},
publisher={Am Soc Neuroradiology}
}
@article{muschelli2015fslr,
title={fslr: Connecting the FSL Software with R},
author={Muschelli, John and Sweeney, Elizabeth and Lindquist, Martin and Crainiceanu, Ciprian},
journal={The R Journal},
volume={7},
number={1},
pages={163--175},
year={2015}
}
@article{sweeney2013oasis,
title={OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI},
author={Sweeney, Elizabeth M and Shinohara, Russell T and Shiee, Navid and Mateen, Farrah J and Chudgar, Avni A and Cuzzocreo, Jennifer L and Calabresi, Peter A and Pham, Dzung L and Reich, Daniel S and Crainiceanu, Ciprian M},
journal={NeuroImage: clinical},
volume={2},
pages={402--413},
year={2013},
publisher={Elsevier}
}
@article{di2014autism,
title={The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism},
author={Di Martino, Adriana and Yan, Chao-Gan and Li, Qingyang and Denio, Erin and Castellanos, Francisco X and Alaerts, Kaat and Anderson, Jeffrey S and Assaf, Michal and Bookheimer, Susan Y and Dapretto, Mirella and others},
journal={Molecular psychiatry},
volume={19},
number={6},
pages={659--667},
year={2014},
publisher={Nature Publishing Group}
}
@article{kennedy2016nitrc,
title={The {NITRC} image repository},
author={Kennedy, David N and Haselgrove, Christian and Riehl, Jon and Preuss, Nina and Buccigrossi, Robert},
journal={NeuroImage},
volume={124},
pages={1069--1073},
year={2016},
publisher={Elsevier}
}
@Article{muschelli2015fslr,
title = {fslr: Connecting the FSL Software with R},
author = {John Muschelli and Elizabeth Sweeney and Martin Lindquist and Ciprian Crainiceanu},
journal = {The R Journal},
volume = {7},
number = {1},
pages = {163--175},
year = {2015},
}