Less is more: balancing noise reduction and data retention in fMRI with data-driven scrubbing

[1]  Matthew W. Mosconi,et al.  Limits to the generalizability of resting-state functional magnetic resonance imaging studies of youth: An examination of ABCD Study® baseline data , 2022, Brain Imaging and Behavior.

[2]  Timothy O. Laumann,et al.  Reproducible brain-wide association studies require thousands of individuals , 2022, Nature.

[3]  D. Barch,et al.  Filtering respiratory motion artifact from resting state fMRI data in infant and toddler populations , 2021, NeuroImage.

[4]  Amanda F. Mejia,et al.  Longitudinal surface-based spatial Bayesian GLM reveals complex trajectories of motor neurodegeneration in ALS , 2021, NeuroImage.

[5]  M. Trivedi,et al.  Pitfalls and recommended strategies and metrics for suppressing motion artifacts in functional MRI , 2021, bioRxiv.

[6]  Amanda F. Mejia,et al.  ciftiTools: A package for reading, writing, visualizing, and manipulating CIFTI files in R , 2021, NeuroImage.

[7]  Zhengwu Zhang,et al.  Which multiband factor should you choose for your resting-state fMRI study? , 2021, NeuroImage.

[8]  Evan M. Gordon,et al.  Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior , 2021, bioRxiv.

[9]  Jonathan D. Power,et al.  Rapid Precision Functional Mapping of Individuals Using Multi-Echo fMRI , 2020, Cell reports.

[10]  Yuankai Huo,et al.  Reconstruction of respiratory variation signals from fMRI data , 2020, NeuroImage.

[11]  Rob J Hyndman,et al.  Dimension Reduction for Outlier Detection Using DOBIN , 2020, J. Comput. Graph. Stat..

[12]  Michael Milham,et al.  Impact of concatenating fMRI data on reliability for functional connectomics , 2020, NeuroImage.

[13]  Karl Rohe,et al.  Vintage Factor Analysis with Varimax Performs Statistical Inference , 2020, Journal of the Royal Statistical Society Series B: Statistical Methodology.

[14]  Emery N. Brown,et al.  Model-based physiological noise removal in fast fMRI , 2020, NeuroImage.

[15]  Philip N. Tubiolo,et al.  Advancing motion denoising of multiband resting-state functional connectivity fMRI data , 2019, NeuroImage.

[16]  Timothy O. Laumann,et al.  Removal of high frequency contamination from motion estimates in single-band fMRI saves data without biasing functional connectivity , 2019, NeuroImage.

[17]  Timothy O. Laumann,et al.  Identifying reproducible individual differences in childhood functional brain networks: An ABCD study , 2019, Developmental Cognitive Neuroscience.

[18]  Dustin Scheinost,et al.  A decade of test-retest reliability of functional connectivity: A systematic review and meta-analysis , 2019, NeuroImage.

[19]  Steven E Petersen,et al.  Evaluating the Prediction of Brain Maturity From Functional Connectivity After Motion Artifact Denoising. , 2019, Cerebral cortex.

[20]  Danielle S Bassett,et al.  Motion artifact in studies of functional connectivity: Characteristics and mitigation strategies , 2019, Human brain mapping.

[21]  Jonathan D. Power,et al.  Distinctions among real and apparent respiratory motions in human fMRI data , 2019, NeuroImage.

[22]  Dinggang Shen,et al.  The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development , 2019, NeuroImage.

[23]  Brian S Caffo,et al.  Modular preprocessing pipelines can reintroduce artifacts into fMRI data , 2018, bioRxiv.

[24]  Anders M. Dale,et al.  Correction of respiratory artifacts in MRI head motion estimates , 2018, bioRxiv.

[25]  Timothy O. Laumann,et al.  Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation , 2018, Neuron.

[26]  Anders M. Dale,et al.  The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites , 2018, Developmental Cognitive Neuroscience.

[27]  Daniel B. Rowe,et al.  Impacts of simultaneous multislice acquisition on sensitivity and specificity in fMRI , 2018, NeuroImage.

[28]  Stephen M. Smith,et al.  Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data , 2017, NeuroImage.

[29]  Jonathan D. Power A simple but useful way to assess fMRI scan qualities , 2017, NeuroImage.

[30]  Christos Davatzikos,et al.  Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity , 2017, NeuroImage.

[31]  Ben D. Fulcher,et al.  An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI , 2017, NeuroImage.

[32]  Evan M. Gordon,et al.  Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI , 2017, bioRxiv.

[33]  Thomas T. Liu,et al.  The global signal in fMRI: Nuisance or Information? , 2017, NeuroImage.

[34]  Soroosh Afyouni,et al.  Insight and inference for DVARS , 2017, NeuroImage.

[35]  Steen Moeller,et al.  Functional Sensitivity of 2D Simultaneous Multi-Slice Echo-Planar Imaging: Effects of Acceleration on g-factor and Physiological Noise , 2017, Front. Neurosci..

[36]  Timothy O. Laumann,et al.  Sources and implications of whole-brain fMRI signals in humans , 2017, NeuroImage.

[37]  César Caballero-Gaudes,et al.  Methods for cleaning the BOLD fMRI signal , 2016, NeuroImage.

[38]  Thomas T. Liu,et al.  Noise contributions to the fMRI signal: An overview , 2016, NeuroImage.

[39]  Tomoki Arichi,et al.  A dedicated neonatal brain imaging system , 2016, Magnetic resonance in medicine.

[40]  P. Matthews,et al.  Multimodal population brain imaging in the UK Biobank prospective epidemiological study , 2016, Nature Neuroscience.

[41]  Julie Josse,et al.  Bayesian Dimensionality Reduction With PCA Using Penalized Semi-Integrated Likelihood , 2016, 1606.05333.

[42]  Terry K Koo,et al.  A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. , 2016, Journal Chiropractic Medicine.

[43]  M. Chun,et al.  Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity , 2015, Nature Neuroscience.

[44]  Amanda F. Mejia,et al.  PCA leverage: outlier detection for high‐dimensional functional magnetic resonance imaging data , 2015, Biostatistics.

[45]  Bernard Ng,et al.  Optimization of rs-fMRI Pre-processing for Enhanced Signal-Noise Separation, Test-Retest Reliability, and Group Discrimination , 2015, NeuroImage.

[46]  Alberto Llera,et al.  ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data , 2015, NeuroImage.

[47]  Jonathan D. Power,et al.  Recent progress and outstanding issues in motion correction in resting state fMRI , 2015, NeuroImage.

[48]  Steen Moeller,et al.  Evaluation of highly accelerated simultaneous multi-slice EPI for fMRI , 2015, NeuroImage.

[49]  Mary Beth Nebel,et al.  Reduction of motion-related artifacts in resting state fMRI using aCompCor , 2014, NeuroImage.

[50]  Steen Moeller,et al.  ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging , 2014, NeuroImage.

[51]  John Suckling,et al.  A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series , 2014, NeuroImage.

[52]  Ludovica Griffanti,et al.  Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers , 2014, NeuroImage.

[53]  Timothy O. Laumann,et al.  Methods to detect, characterize, and remove motion artifact in resting state fMRI , 2014, NeuroImage.

[54]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[55]  Mark Jenkinson,et al.  The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.

[56]  Joshua Carp,et al.  Optimizing the order of operations for movement scrubbing: Comment on Power et al. , 2013, NeuroImage.

[57]  R. Cameron Craddock,et al.  A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics , 2013, NeuroImage.

[58]  Hang Joon Jo,et al.  Effective Preprocessing Procedures Virtually Eliminate Distance-Dependent Motion Artifacts in Resting State FMRI , 2013, J. Appl. Math..

[59]  R. Tibshirani Adaptive piecewise polynomial estimation via trend filtering , 2013, 1304.2986.

[60]  Simon B. Eickhoff,et al.  An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data , 2013, NeuroImage.

[61]  Simon B. Eickhoff,et al.  One-year test–retest reliability of intrinsic connectivity network fMRI in older adults , 2012, NeuroImage.

[62]  Mark A. Elliott,et al.  Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth , 2012, NeuroImage.

[63]  Abraham Z. Snyder,et al.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.

[64]  Mert R. Sabuncu,et al.  The influence of head motion on intrinsic functional connectivity MRI , 2012, NeuroImage.

[65]  P. Wentzell,et al.  Fast and simple methods for the optimization of kurtosis used as a projection pursuit index. , 2011, Analytica chimica acta.

[66]  Mark Chiew,et al.  Spin-history artifact during functional MRI: Potential for adaptive correction. , 2011, Medical physics.

[67]  Catie Chang,et al.  Resting-state fMRI can reliably map neural networks in children , 2011, NeuroImage.

[68]  Rex E. Jung,et al.  A Baseline for the Multivariate Comparison of Resting-State Networks , 2011, Front. Syst. Neurosci..

[69]  A. Snyder,et al.  Longitudinal analysis of neural network development in preterm infants. , 2010, Cerebral cortex.

[70]  Xi-Nian Zuo,et al.  Reliable intrinsic connectivity networks: Test–retest evaluation using ICA and dual regression approach , 2010, NeuroImage.

[71]  M. Fukunaga,et al.  Sources of functional magnetic resonance imaging signal fluctuations in the human brain at rest: a 7 T study. , 2009, Magnetic resonance imaging.

[72]  B. Biswal,et al.  The resting brain: unconstrained yet reliable. , 2009, Cerebral cortex.

[73]  R. Tibshirani,et al.  A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. , 2009, Biostatistics.

[74]  Stephen P. Boyd,et al.  1 Trend Filtering , 2009, SIAM Rev..

[75]  Steven C. R. Williams,et al.  Measuring fMRI reliability with the intra-class correlation coefficient , 2009, NeuroImage.

[76]  Kevin Murphy,et al.  The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? , 2009, NeuroImage.

[77]  M. Lindquist The Statistical Analysis of fMRI Data. , 2008, 0906.3662.

[78]  Yu Li,et al.  Respiratory Noise Correction Using Phase Information , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

[79]  Thomas T. Liu,et al.  A component based noise correction method (CompCor) for BOLD and perfusion based fMRI , 2007, NeuroImage.

[80]  David M. Rocke,et al.  The Distribution of Robust Distances , 2005 .

[81]  X Hu,et al.  Retrospective estimation and correction of physiological artifacts in fMRI by direct extraction of physiological activity from MR data , 1996, Magnetic resonance in medicine.

[82]  J. Fleiss,et al.  Intraclass correlations: uses in assessing rater reliability. , 1979, Psychological bulletin.

[83]  V. Barnett,et al.  Applied Linear Statistical Models , 1975 .

[84]  J. Murray,et al.  HANDBOOK OF PSYCHOLOGY , 1951 .

[85]  Rory A. Fisher,et al.  The Moments of the Distribution for Normal Samples of Measures of Departure from Normality , 1930 .

[86]  Mark Chiew,et al.  Spin-history artifact during functional MRI: potential for adaptive correction. , 2011, Medical physics.

[87]  Hang,et al.  Resting-state fMRI can reliably map neural networks in children , 2010 .

[88]  Tom Minka,et al.  Automatic Choice of Dimensionality for PCA , 2000, NIPS.

[89]  P. Rousseeuw,et al.  Unmasking Multivariate Outliers and Leverage Points , 1990 .

[90]  Human Brain Mapping 6:160–188(1998) � Analysis of fMRI Data by Blind Separation Into Independent Spatial Components , 2022 .