
@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{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{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{phamScrubbingLess2023,
  title={Less is more: balancing noise reduction and data retention in fMRI with data-driven scrubbing},
  author={Pham, Damon and McDonald, Daniel and Ding, Lei and Nebel, Mary Beth and Mejia, Amanda},
  journal={NeuroImage},
  pages={119972},
  year={2023},
  publisher={Elsevier}
}

@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{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}
}

@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}
}
