Evaluation of confound regression strategies for the mitigation of micromovement artifact in studies of dynamic resting-state functional connectivity and multilayer network modularity
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Danielle S Bassett | Theodore D Satterthwaite | David M Lydon-Staley | Rastko Ciric | D. Bassett | R. Ciric | T. Satterthwaite | D. Lydon‐Staley
[1] H. Hakonarson,et al. Incidental Findings in Youths Volunteering for Brain MRI Research , 2013, American Journal of Neuroradiology.
[2] Danielle S. Bassett,et al. Cognitive Network Neuroscience , 2015, Journal of Cognitive Neuroscience.
[3] Karl J. Friston,et al. Movement‐Related effects in fMRI time‐series , 1996, Magnetic resonance in medicine.
[4] Hang Joon Jo,et al. The perils of global signal regression for group comparisons: a case study of Autism Spectrum Disorders , 2013, Front. Hum. Neurosci..
[5] Li Yao,et al. The dynamic characteristics of the anterior cingulate cortex in resting-state fMRI of patients with depression. , 2018, Journal of affective disorders.
[6] J. H. Steiger. Tests for comparing elements of a correlation matrix. , 1980 .
[7] Jonathan D. Power,et al. Recent progress and outstanding issues in motion correction in resting state fMRI , 2015, NeuroImage.
[8] Danielle S Bassett,et al. Learning-induced autonomy of sensorimotor systems , 2014, Nature Neuroscience.
[9] Eswar Damaraju,et al. Tracking whole-brain connectivity dynamics in the resting state. , 2014, Cerebral cortex.
[10] Timothy O. Laumann,et al. Data Quality Influences Observed Links Between Functional Connectivity and Behavior , 2017, Cerebral cortex.
[11] 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.
[12] Christos Davatzikos,et al. Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity , 2017, NeuroImage.
[13] Stephen M. Smith,et al. Temporally-independent functional modes of spontaneous brain activity , 2012, Proceedings of the National Academy of Sciences.
[14] Mark A. Elliott,et al. The Philadelphia Neurodevelopmental Cohort: A publicly available resource for the study of normal and abnormal brain development in youth , 2016, NeuroImage.
[15] Janet Elizabeth Hope. Open Source , 2017, Encyclopedia of GIS.
[16] Danielle S Bassett,et al. Brain graphs: graphical models of the human brain connectome. , 2011, Annual review of clinical psychology.
[17] Denise C. Park,et al. Decreased segregation of brain systems across the healthy adult lifespan , 2014, Proceedings of the National Academy of Sciences.
[18] J. Duyn,et al. Time-varying functional network information extracted from brief instances of spontaneous brain activity , 2013, Proceedings of the National Academy of Sciences.
[19] Mert R. Sabuncu,et al. The influence of head motion on intrinsic functional connectivity MRI , 2012, NeuroImage.
[20] O. Sporns,et al. Network neuroscience , 2017, Nature Neuroscience.
[21] Olaf Sporns,et al. Cerebral cartography and connectomics , 2015, Philosophical Transactions of the Royal Society B: Biological Sciences.
[22] Kevin Murphy,et al. Towards a consensus regarding global signal regression for resting state functional connectivity MRI , 2017, NeuroImage.
[23] Edward T. Bullmore,et al. On the use of correlation as a measure of network connectivity , 2012, NeuroImage.
[24] Jessica R. Cohen. The behavioral and cognitive relevance of time-varying, dynamic changes in functional connectivity , 2017, NeuroImage.
[25] Kevin Murphy,et al. Resting-state fMRI confounds and cleanup , 2013, NeuroImage.
[26] Beatriz Luna,et al. The nuisance of nuisance regression: Spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity , 2013, NeuroImage.
[27] Evan M. Gordon,et al. On the Stability of BOLD fMRI Correlations , 2016, Cerebral cortex.
[28] Alberto Llera,et al. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data , 2015, NeuroImage.
[29] Mason A. Porter,et al. Robust Detection of Dynamic Community Structure in Networks , 2012, Chaos.
[30] Danielle S. Bassett,et al. Modeling and interpreting mesoscale network dynamics , 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 .
[32] Martin A. Lindquist,et al. Dynamic connectivity regression: Determining state-related changes in brain connectivity , 2012, NeuroImage.
[33] Arno Klein,et al. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.
[34] G. Deco,et al. Dynamic functional connectivity reveals altered variability in functional connectivity among patients with major depressive disorder , 2016, Human brain mapping.
[35] Yu Cao,et al. Multi-atlas Segmentation with Learning-Based Label Fusion , 2014, MLMI.
[36] Hang Joon Jo,et al. Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression , 2012, Brain Connect..
[37] Christos Davatzikos,et al. Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth , 2013, NeuroImage.
[38] Lena S. Geiger,et al. Dynamic brain network reconfiguration as a potential schizophrenia genetic risk mechanism modulated by NMDA receptor function , 2016, Proceedings of the National Academy of Sciences.
[39] M. V. D. Heuvel,et al. Exploring the brain network: A review on resting-state fMRI functional connectivity , 2010, European Neuropsychopharmacology.
[40] Timothy O. Laumann,et al. Functional Network Organization of the Human Brain , 2011, Neuron.
[41] Danielle S. Bassett,et al. Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction , 2015, PloS one.
[42] Birk Diedenhofen,et al. cocor: A Comprehensive Solution for the Statistical Comparison of Correlations , 2015, PloS one.
[43] Eswar Damaraju,et al. The effect of preprocessing in dynamic functional network connectivity used to classify mild traumatic brain injury , 2017, Brain and behavior.
[44] Danielle S Bassett,et al. Evolution of network architecture in a granular material under compression. , 2016, Physical review. E.
[45] A. Belger,et al. Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia , 2014, NeuroImage: Clinical.
[46] M. Schölvinck,et al. Neural basis of global resting-state fMRI activity , 2010, Proceedings of the National Academy of Sciences.
[47] César Caballero-Gaudes,et al. Methods for cleaning the BOLD fMRI signal , 2016, NeuroImage.
[48] P. DeRosse,et al. Dynamic Functional Connectivity States Reflecting Psychotic-like Experiences. , 2017, Biological psychiatry. Cognitive neuroscience and neuroimaging.
[49] Ewald Moser,et al. On the origin of respiratory artifacts in BOLD-EPI of the human brain. , 2002, Magnetic resonance imaging.
[50] Timothy O. Laumann,et al. Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. , 2016, Cerebral cortex.
[51] Timothy O. Laumann,et al. Sources and implications of whole-brain fMRI signals in humans , 2017, NeuroImage.
[52] Bharat B. Biswal,et al. Resting state fMRI: A personal history , 2012, NeuroImage.
[53] A. Mackey,et al. Resting-State fMRI , 2014, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.
[54] V. Calhoun,et al. The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery , 2014, Neuron.
[55] Andreas Heinz,et al. Test–retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures , 2012, NeuroImage.
[56] Kent A. Kiehl,et al. A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia , 2010, Magnetic Resonance Materials in Physics, Biology and Medicine.
[57] Jonathan D. Power,et al. Functional Brain Networks Develop from a “Local to Distributed” Organization , 2009, PLoS Comput. Biol..
[58] Danielle S Bassett,et al. Cohesive network reconfiguration accompanies extended training , 2017, Human brain mapping.
[59] Danielle S Bassett,et al. Mitigating head motion artifact in functional connectivity MRI , 2018, Nature Protocols.
[60] Danielle S. Bassett,et al. Positive affect, surprise, and fatigue are correlates of network flexibility , 2017, Scientific Reports.
[61] Brian B. Avants,et al. N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.
[62] Edward T. Bullmore,et al. Neuroinformatics Original Research Article , 2022 .
[63] Dinggang Shen,et al. Test-Retest Reliability of “High-Order” Functional Connectivity in Young Healthy Adults , 2017, Front. Neurosci..
[64] R W Cox,et al. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.
[65] Christos Davatzikos,et al. Neuroimaging of the Philadelphia Neurodevelopmental Cohort , 2014, NeuroImage.
[66] Kathleen R. Merikangas,et al. Prevalence and Treatment of Mental Disorders Among US Children in the 2001–2004 NHANES , 2010, Pediatrics.
[67] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[68] Dimitri Van De Ville,et al. The dynamic functional connectome: State-of-the-art and perspectives , 2017, NeuroImage.
[69] R. Poldrack,et al. Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attention , 2016, Proceedings of the National Academy of Sciences.
[70] Thomas T. Liu,et al. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI , 2007, NeuroImage.
[71] Joaquín Goñi,et al. Changes in structural and functional connectivity among resting-state networks across the human lifespan , 2014, NeuroImage.
[72] Mary Beth Nebel,et al. Reduction of motion-related artifacts in resting state fMRI using aCompCor , 2014, NeuroImage.
[73] Hang Joon Jo,et al. Mapping sources of correlation in resting state FMRI, with artifact detection and removal , 2010, NeuroImage.
[74] Dimitri Van De Ville,et al. On spurious and real fluctuations of dynamic functional connectivity during rest , 2015, NeuroImage.
[75] C. Grady,et al. Age differences in the functional interactions among the default, frontoparietal control, and dorsal attention networks , 2016, Neurobiology of Aging.
[76] Antonio Napolitano,et al. Test-retest reliability of graph metrics of resting state MRI functional brain networks: A review , 2015, Journal of Neuroscience Methods.
[77] D. Bassett,et al. Dynamic reconfiguration of frontal brain networks during executive cognition in humans , 2015, Proceedings of the National Academy of Sciences.
[78] Jukka-Pekka Onnela,et al. Community Structure in Time-Dependent, Multiscale, and Multiplex Networks , 2009, Science.
[79] R. Cameron Craddock,et al. A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics , 2013, NeuroImage.
[80] E. Bullmore,et al. Human brain networks in health and disease , 2009, Current opinion in neurology.
[81] Danielle S. Bassett,et al. Brain state expression and transitions are related to complex executive cognition in normative neurodevelopment , 2018, NeuroImage.
[82] Jonathan D. Power,et al. Temporal interpolation alters motion in fMRI scans: Magnitudes and consequences for artifact detection , 2017, PloS one.
[83] 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.
[84] Simon W. Moore,et al. Efficient Physical Embedding of Topologically Complex Information Processing Networks in Brains and Computer Circuits , 2010, PLoS Comput. Biol..
[85] J. Morton,et al. It's a matter of time: Reframing the development of cognitive control as a modification of the brain's temporal dynamics , 2015, Developmental Cognitive Neuroscience.
[86] B. Biswal,et al. Dynamic brain functional connectivity modulated by resting-state networks , 2013, Brain Structure and Function.
[87] Michael Brady,et al. Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.
[88] Danielle S. Bassett,et al. Evolution of brain network dynamics in neurodevelopment , 2017, Network Neuroscience.
[89] Richard F. Betzel,et al. Modular Brain Networks. , 2016, Annual review of psychology.
[90] Abraham Z. Snyder,et al. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.
[91] Alan C. Evans,et al. Revealing modular architecture of human brain structural networks by using cortical thickness from MRI. , 2008, Cerebral cortex.
[92] Brian B. Avants,et al. An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data , 2011, Neuroinformatics.
[93] J.C. Cheng,et al. Slow-5 dynamic functional connectivity reflects the capacity to sustain cognitive performance during pain , 2017, NeuroImage.
[94] Arno Klein,et al. Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements , 2014, NeuroImage.
[95] Arno Klein,et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration , 2011, NeuroImage.
[96] Ben D. Fulcher,et al. An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI , 2017, NeuroImage.
[97] David A. Leopold,et al. Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.
[98] Danielle S Bassett,et al. Motion artifact in studies of functional connectivity: Characteristics and mitigation strategies , 2019, Human brain mapping.
[99] Bruce Fischl,et al. Accurate and robust brain image alignment using boundary-based registration , 2009, NeuroImage.
[100] Swathi P. Iyer,et al. Distinct neural signatures detected for ADHD subtypes after controlling for micro-movements in resting state functional connectivity MRI data , 2012, Front. Syst. Neurosci..
[101] Ludovica Griffanti,et al. Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers , 2014, NeuroImage.
[102] Scott T. Grafton,et al. Dynamic reconfiguration of human brain networks during learning , 2010, Proceedings of the National Academy of Sciences.
[103] O. Sporns,et al. Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.
[104] Z. Yao,et al. Resting-State Time-Varying Analysis Reveals Aberrant Variations of Functional Connectivity in Autism , 2016, Front. Hum. Neurosci..
[105] B. T. Thomas Yeo,et al. Interpreting temporal fluctuations in resting-state functional connectivity MRI , 2017, NeuroImage.
[106] Andreas A Ioannides,et al. Dynamic functional connectivity , 2007, Current Opinion in Neurobiology.
[107] Aaron Kucyi,et al. Dynamic functional connectivity of the default mode network tracks daydreaming , 2014, NeuroImage.
[108] Danielle S. Bassett,et al. Dynamic graph metrics: Tutorial, toolbox, and tale , 2017, NeuroImage.
[109] Sarah Feldt Muldoon,et al. Network and Multilayer Network Approaches to Understanding Human Brain Dynamics , 2016, Philosophy of Science.