
@article{afyouniInsightInferenceDVARS2018,
  ids = {afyouniInsightInferenceDVARS2018a},
  title = {Insight and Inference for {{DVARS}}},
  author = {Afyouni, Soroosh and Nichols, Thomas E.},
  year = {2018},
  month = may,
  journal = {NeuroImage},
  volume = {172},
  pages = {291--312},
  issn = {1053-8119},
  doi = {10.1016/j.neuroimage.2017.12.098}
 }

@article{ahmedDiscreteCosineTransform1974,
  ids = {ahmedDiscreteCosineTransform1974a},
  title = {Discrete {{Cosine Transform}}},
  author = {Ahmed, N. and Natarajan, T. and Rao, K.R.},
  year = {1974},
  month = jan,
  journal = {IEEE Transactions on Computers},
  volume = {C-23},
  number = {1},
  pages = {90--93},
  issn = {2326-3814},
  doi = {10.1109/T-C.1974.223784}
}

@article{behzadiComponentBasedNoise2007,
  ids = {behzadiComponentBasedNoise2007a},
  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.},
  year = {2007},
  month = aug,
  journal = {NeuroImage},
  volume = {37},
  number = {1},
  pages = {90--101},
  issn = {1053-8119},
  doi = {10.1016/j.neuroimage.2007.04.042}
}

@inproceedings{grabnerSymmetricAtlasingModel2006,
  title = {Symmetric {{Atlasing}} and {{Model Based Segmentation}}: {{An Application}} to the {{Hippocampus}} in {{Older Adults}}},
  shorttitle = {Symmetric {{Atlasing}} and {{Model Based Segmentation}}},
  booktitle = {Medical {{Image Computing}} and {{Computer}}-{{Assisted Intervention}} \textendash{} {{MICCAI}} 2006},
  author = {Grabner, G{\"u}nther and Janke, Andrew L. and Budge, Marc M. and Smith, David and Pruessner, Jens and Collins, D. Louis},
  editor = {Larsen, Rasmus and Nielsen, Mads and Sporring, Jon},
  year = {2006},
  series = {Lecture {{Notes}} in {{Computer Science}}},
  pages = {58--66},
  publisher = {{Springer}},
  address = {{Berlin, Heidelberg}},
  doi = {10.1007/11866763_8}
}

@article{jenkinsonFSL2012,
  title = {{{FSL}}},
  author = {Jenkinson, Mark and Beckmann, Christian F. and Behrens, Timothy E. J. and Woolrich, Mark W. and Smith, Stephen M.},
  year = {2012},
  month = aug,
  journal = {NeuroImage},
  series = {20 {{YEARS OF fMRI}}},
  volume = {62},
  number = {2},
  pages = {782--790},
  issn = {1053-8119},
  doi = {10.1016/j.neuroimage.2011.09.015}
}

@article{kimEllTrendFiltering2009,
  title = {\$\textbackslash ell\_1\$ {{Trend Filtering}}},
  author = {Kim, Seung-Jean and Koh, Kwangmoo and Boyd, Stephen and Gorinevsky, Dimitry},
  year = {2009},
  month = may,
  journal = {SIAM Review},
  volume = {51},
  number = {2},
  pages = {339--360},
  publisher = {{Society for Industrial and Applied Mathematics}},
  issn = {0036-1445},
  doi = {10.1137/070690274}
}

@article{lindquistModularPreprocessingPipelines2019,
  title = {Modular Preprocessing Pipelines Can Reintroduce Artifacts into {{fMRI}} Data},
  author = {Lindquist, Martin A. and Geuter, Stephan and Wager, Tor D. and Caffo, Brian S.},
  year = {2019},
  journal = {Human Brain Mapping},
  volume = {40},
  number = {8},
  pages = {2358--2376},
  issn = {1097-0193},
  doi = {10.1002/hbm.24528},
  abstract = {The preprocessing pipelines typically used in both task and resting-state functional magnetic resonance imaging (rs-fMRI) analysis are modular in nature: They are composed of a number of separate filtering/regression steps, including removal of head motion covariates and band-pass filtering, performed sequentially and in a flexible order. In this article, we illustrate the shortcomings of this approach, as we show how later preprocessing steps can reintroduce artifacts previously removed from the data in prior preprocessing steps. We show that each regression step is a geometric projection of data onto a subspace, and that performing a sequence of projections can move the data into subspaces no longer orthogonal to those previously removed, reintroducing signal related to nuisance covariates. Thus, linear filtering operations are not commutative, and the order in which the preprocessing steps are performed is critical. These issues can arise in practice when any combination of standard preprocessing steps including motion regression, scrubbing, component-based correction, physiological correction, global signal regression, and temporal filtering are performed sequentially. In this work, we focus primarily on rs-fMRI. We illustrate the problem both theoretically and empirically through application to a test\textendash retest rs-fMRI data set, and suggest remedies. These include (a) combining all steps into a single linear filter, or (b) sequential orthogonalization of covariates/linear filters performed in series.},
  copyright = {\textcopyright{} 2019 Wiley Periodicals, Inc.}
}

@article{mejiaPCALeverageOutlier2017,
  ids = {mejiaPCALeverageOutlier2017a,mejiaPCALeverageOutlier2017b,mejiaPCALeverageOutlier2017c},
  title = {{{PCA}} Leverage: Outlier Detection for High-Dimensional Functional Magnetic Resonance Imaging Data},
  shorttitle = {{{PCA}} Leverage},
  author = {Mejia, Amanda F. and Nebel, Mary Beth and Eloyan, Ani and Caffo, Brian and Lindquist, Martin A.},
  year = {2017},
  month = jul,
  journal = {Biostatistics (Oxford, England)},
  volume = {18},
  number = {3},
  pages = {521--536},
  publisher = {{Oxford Academic}},
  issn = {1468-4357},
  doi = {10.1093/biostatistics/kxw050},
  abstract = {Outlier detection for high-dimensional (HD) data is a popular topic in modern statistical research. However, one source of HD data that has received relatively little attention is functional magnetic resonance images (fMRI), which consists of hundreds of thousands of measurements sampled at hundreds of time points. At a time when the availability of fMRI data is rapidly growing-primarily through large, publicly available grassroots datasets-automated quality control and outlier detection methods are greatly needed. We propose principal components analysis (PCA) leverage and demonstrate how it can be used to identify outlying time points in an fMRI run. Furthermore, PCA leverage is a measure of the influence of each observation on the estimation of principal components, which are often of interest in fMRI data. We also propose an alternative measure, PCA robust distance, which is less sensitive to outliers and has controllable statistical properties. The proposed methods are validated through simulation studies and are shown to be highly accurate. We also conduct a reliability study using resting-state fMRI data from the Autism Brain Imaging Data Exchange and find that removal of outliers using the proposed methods results in more reliable estimation of subject-level resting-state networks using independent components analysis.},
  language = {eng},
  pmcid = {PMC5862350},
  pmid = {28334131}
}

@article{muschelliReductionMotionrelatedArtifacts2014,
  ids = {muschelliReductionMotionrelatedArtifacts2014a},
  title = {Reduction of Motion-Related Artifacts in Resting State {{fMRI}} Using {{aCompCor}}},
  author = {Muschelli, John and Nebel, Mary Beth and Caffo, Brian S. and Barber, Anita D. and Pekar, James J. and Mostofsky, Stewart H.},
  year = {2014},
  month = aug,
  journal = {NeuroImage},
  volume = {96},
  pages = {22--35},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2014.03.028}
}

@article{phamProjectionScrubbingMore2022,
  title = {Less is more: balancing noise reduction and data retention in fMRI with projection scrubbing},
  shorttitle = {Projection Scrubbing},
  author = {Pham, Damon and McDonald, Daniel and Ding, Lei and Nebel, Mary Beth and Mejia, Amanda},
  year = {2022},
  month = jul
}

@article{powerSpuriousSystematicCorrelations2012,
  ids = {powerSpuriousSystematicCorrelations2012a},
  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},
  year = {2012},
  month = feb,
  journal = {Neuroimage},
  volume = {59},
  number = {3},
  pages = {2142--2154},
  issn = {1053-8119},
  doi = {10.1016/j.neuroimage.2011.10.018}
}

@article{sobczykBayesianDimensionalityReduction2017,
  title = {Bayesian {{Dimensionality Reduction With PCA Using Penalized Semi}}-{{Integrated Likelihood}}},
  author = {Sobczyk, Piotr and Bogdan, Ma{\l}gorzata and Josse, Julie},
  year = {2017},
  month = oct,
  journal = {Journal of Computational and Graphical Statistics},
  volume = {26},
  number = {4},
  pages = {826--839},
  publisher = {{Taylor \& Francis}},
  issn = {1061-8600},
  doi = {10.1080/10618600.2017.1340302}
}

@article{tibshiraniAdaptivePiecewisePolynomial2014,
  title = {Adaptive Piecewise Polynomial Estimation via Trend Filtering},
  author = {Tibshirani, Ryan J.},
  year = {2014},
  month = feb,
  journal = {The Annals of Statistics},
  volume = {42},
  number = {1},
  pages = {285--323},
  publisher = {{Institute of Mathematical Statistics}},
  issn = {0090-5364, 2168-8966},
  doi = {10.1214/13-AOS1189}
}


