A low-rank multivariate general linear model for multi-subject fMRI data and a non-convex optimization algorithm for brain response comparison
暂无分享,去创建一个
Tingting Zhang | Minh Pham | Jianhui Sun | James A. Coan | Huazhang Li | Marlen Z. Gonzalez | Yinge Sun | Guofen Yan | Tingting Zhang | J. Coan | G. Yan | Huazhang Li | Jianhui Sun | Yinge Sun | Minh Pham
[1] Ying Guo,et al. Modeling Covariate Effects in Group Independent Component Analysis with Applications to Functional Magnetic Resonance Imaging , 2014, 1402.4239.
[2] W D Hairston,et al. Evaluating the impact of spatio-temporal smoothness constraints on the BOLD hemodynamic response function estimation: an analysis based on Tikhonov regularization , 2009, Physiological measurement.
[3] Xiaodong Lin,et al. Alternating linearization for structured regularization problems , 2011, J. Mach. Learn. Res..
[4] R. Buxton,et al. A Model for the Coupling between Cerebral Blood Flow and Oxygen Metabolism during Neural Stimulation , 1997, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.
[5] Kathrin Klamroth,et al. Biconvex sets and optimization with biconvex functions: a survey and extensions , 2007, Math. Methods Oper. Res..
[6] S. Wood. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models , 2011 .
[7] J. Stock,et al. Forecasting Using Principal Components From a Large Number of Predictors , 2002 .
[8] R. D'Arcy,et al. White versus gray matter: fMRI hemodynamic responses show similar characteristics, but differ in peak amplitude , 2012, BMC Neuroscience.
[9] M. Lindquist. The Statistical Analysis of fMRI Data. , 2008, 0906.3662.
[10] V. Calhoun,et al. Functional network connectivity during rest and task conditions: A comparative study , 2013, Human brain mapping.
[11] Richard J. Davidson,et al. PSYCHOLOGICAL SCIENCE Research Article Lending a Hand Social Regulation of the Neural Response to Threat , 2022 .
[12] Marina Vannucci,et al. A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data , 2016 .
[13] M. Lindquist,et al. Validity and power in hemodynamic response modeling: A comparison study and a new approach , 2007, Human brain mapping.
[14] Hans Knutsson,et al. Detection and detrending in fMRI data analysis , 2004, NeuroImage.
[15] G. Glover. Deconvolution of Impulse Response in Event-Related BOLD fMRI1 , 1999, NeuroImage.
[16] V. D. Calhoun,et al. fMRI analysis with the general linear model: removal of latency-induced amplitude bias by incorporation of hemodynamic derivative terms , 2004, NeuroImage.
[17] Mark Jarmasz,et al. Exploratory data analysis reveals visuovisual interhemispheric transfer in functional magnetic resonance imaging , 2006, Magnetic resonance in medicine.
[18] Grace Wahba,et al. Spline Models for Observational Data , 1990 .
[19] S. Balqis Samdin,et al. A Unified Estimation Framework for State-Related Changes in Effective Brain Connectivity , 2017, IEEE Transactions on Biomedical Engineering.
[20] Sadasivan Puthusserypady,et al. Analysis of fMRI Data With Drift: Modified General Linear Model and Bayesian Estimator , 2008, IEEE Transactions on Biomedical Engineering.
[21] Augusto Aubry,et al. Maximum Likelihood Estimation of a Structured Covariance Matrix With a Condition Number Constraint , 2012, IEEE Transactions on Signal Processing.
[22] Steven D Beyea,et al. Detecting functional magnetic resonance imaging activation in white matter: Interhemispheric transfer across the corpus callosum , 2008, BMC Neuroscience.
[23] Mark D'Esposito,et al. Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses , 2004, NeuroImage.
[24] Nicholas Ayache,et al. Parcellation of brain images with anatomical and functional constraints for fMRI data analysis , 2002, Proceedings IEEE International Symposium on Biomedical Imaging.
[25] R. Eubank. Nonparametric Regression and Spline Smoothing , 1999 .
[26] Karl J. Friston,et al. Statistical parametric maps in functional imaging: A general linear approach , 1994 .
[27] Hernando Ombao,et al. Multi-Scale Factor Analysis of High-Dimensional Functional Connectivity in Brain Networks , 2020, IEEE Transactions on Network Science and Engineering.
[28] A M Dale,et al. Optimal experimental design for event‐related fMRI , 1999, Human brain mapping.
[29] Laurent Risser,et al. Spatially adaptive mixture modeling for analysis of fMRI time series , 2009, NeuroImage.
[30] Karl J. Friston,et al. Analysis of functional MRI time‐series , 1994, Human Brain Mapping.
[31] J. Allen,et al. The relation of attachment security to adolescents' paternal and peer relationships, depression, and externalizing behavior. , 2007, Child development.
[32] Martin J. Wainwright,et al. Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions , 2011, ICML.
[33] Genevera I. Allen,et al. Journal of the American Statistical Association a Generalized Least-square Matrix Decomposition a Generalized Least-square Matrix Decomposition , 2022 .
[34] Brian Knutson,et al. FMRI Visualization of Brain Activity during a Monetary Incentive Delay Task , 2000, NeuroImage.
[35] Chee-Ming Ting,et al. Modeling Effective Connectivity in High-Dimensional Cortical Source Signals , 2016, IEEE Journal of Selected Topics in Signal Processing.
[36] M. Srivastava,et al. Estimation and testing in general multivariate linear models with Kronecker product covariance structure , 2009 .
[37] Karl J. Friston,et al. Analysis of fMRI Time-Series Revisited , 1995, NeuroImage.
[38] Richard G. Baraniuk,et al. Sparse Bilinear Logistic Regression , 2014, ArXiv.
[39] Alan C. Evans,et al. A General Statistical Analysis for fMRI Data , 2000, NeuroImage.
[40] R. Todd Ogden,et al. Smoothing parameter selection for a class of semiparametric linear models , 2009 .
[41] C M Zhang,et al. A comparative study of one‐level and two‐level semiparametric estimation of hemodynamic response function for fMRI data , 2007, Statistics in medicine.
[42] Marina Vannucci,et al. A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses , 2014, NeuroImage.
[43] Karl J. Friston,et al. To Smooth or Not to Smooth? Bias and Efficiency in fMRI Time-Series Analysis , 2000, NeuroImage.
[44] Stephen M. Smith,et al. Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.
[45] André J. Szameitat,et al. The functional magnetic resonance imaging (fMRI) procedure as experienced by healthy participants and stroke patients – A pilot study , 2009, BMC Medical Imaging.
[46] Paul J. Laurienti,et al. The impact of temporal regularization on estimates of the BOLD hemodynamic response function: A comparative analysis , 2008, NeuroImage.
[47] Thomas M. Talavage,et al. Simulation of human respiration in fMRI with a mechanical model , 2002, IEEE Transactions on Biomedical Engineering.
[48] J. Coan,et al. Childhood maternal support and social capital moderate the regulatory impact of social relationships in adulthood. , 2013, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.
[49] Michael Brady,et al. Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.
[50] Ying Guo,et al. Group Independent Component Analysis of Multi-subject fMRI Data: Connections and Distinctions between Two Methods , 2008, 2008 International Conference on BioMedical Engineering and Informatics.
[51] Erin L. Mazerolle,et al. Does functional MRI detect activation in white matter? A review of emerging evidence, issues, and future directions , 2014, Front. Neurosci..
[52] Chee-Ming Ting,et al. Multi-Scale Factor Analysis of High-Dimensional Functional Connectivity in Brain Networks , 2017, IEEE Transactions on Network Science and Engineering.
[53] James A. Coan,et al. Adult attachment and the brain , 2010 .
[54] Bruce Fischl,et al. A Role for the Human Dorsal Anterior Cingulate Cortex in Fear Expression , 2007, Biological Psychiatry.
[55] R. Turner,et al. Event-Related fMRI: Characterizing Differential Responses , 1998, NeuroImage.
[56] P. Reiss,et al. Functional Principal Component Regression and Functional Partial Least Squares , 2007 .
[57] J B Poline,et al. Joint detection-estimation of brain activity in functional MRI: a Multichannel Deconvolution solution , 2005, IEEE Transactions on Signal Processing.
[58] G. Robinson. That BLUP is a Good Thing: The Estimation of Random Effects , 1991 .
[59] L. K. Hansen,et al. Plurality and Resemblance in fMRI Data Analysis , 1999, NeuroImage.
[60] Gabriele Polonara,et al. Topographical organization of human corpus callosum: An fMRI mapping study , 2011, Brain Research.
[61] T. Braver,et al. BOLD Correlates of Trial-by-Trial Reaction Time Variability in Gray and White Matter: A Multi-Study fMRI Analysis , 2009, PloS one.
[62] A. Dale,et al. Dorsal anterior cingulate cortex: A role in reward-based decision making , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[63] Thomas E. Nichols,et al. Modeling Inter‐Subject Variability in fMRI Activation Location: A Bayesian Hierarchical Spatial Model , 2009, Biometrics.
[64] J. -B. Poline,et al. Estimating the Delay of the fMRI Response , 2002, NeuroImage.
[65] Karl J. Friston,et al. A neuromodulatory role for the human amygdala in processing emotional facial expressions. , 1998, Brain : a journal of neurology.
[66] J. Duyn,et al. Investigation of Low Frequency Drift in fMRI Signal , 1999, NeuroImage.
[67] R. Potthoff,et al. A generalized multivariate analysis of variance model useful especially for growth curve problems , 1964 .
[68] Li Tang,et al. A Hierarchical Model for Probabilistic Independent Component Analysis of Multi‐Subject fMRI Studies , 2013, Biometrics.
[69] Ryan C. N. D'Arcy,et al. Functional mapping in the corpus callosum: A 4T fMRI study of white matter , 2011, NeuroImage.
[70] M. Irwin,et al. Dorsal Anterior Cingulate Cortex Responses to Repeated Social Evaluative Feedback in Young Women with and without a History of Depression , 2016, Front. Behav. Neurosci..
[71] Olivier D. Faugeras,et al. Feature Detection in fMRI Data: The Information Bottleneck Approach , 2003, MICCAI.
[72] Shuicheng Yan,et al. Online Robust PCA via Stochastic Optimization , 2013, NIPS.
[73] Lotfi Chaâri,et al. Hemodynamic-Informed Parcellation of fMRI Data in a Joint Detection Estimation Framework , 2012, MICCAI.
[74] K. Gabriel,et al. Generalised bilinear regression , 1998 .
[75] Kunpeng Li,et al. STATISTICAL ANALYSIS OF FACTOR MODELS OF HIGH DIMENSION , 2012, 1205.6617.
[76] Jin Fan,et al. Preparatory activity and connectivity in dorsal anterior cingulate cortex for cognitive control , 2011, NeuroImage.
[77] J. Pekar,et al. A method for making group inferences from functional MRI data using independent component analysis , 2001, Human brain mapping.
[78] Bertrand Thirion,et al. A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI , 2008, NeuroImage.
[79] Fan Li,et al. A semi-parametric model of the hemodynamic response for multi-subject fMRI data , 2013, NeuroImage.
[80] Yehuda Koren,et al. Matrix Factorization Techniques for Recommender Systems , 2009, Computer.
[81] Ron Borowsky,et al. Characterizing the functional MRI response using Tikhonov regularization , 2007, Statistics in medicine.
[82] Andrzej Ruszczynski,et al. Proximal Decomposition Via Alternating Linearization , 1999, SIAM J. Optim..
[83] James A. Coan,et al. The Social Regulation of Emotion , 2011 .
[84] T. Adali,et al. Unmixing fMRI with independent component analysis , 2006, IEEE Engineering in Medicine and Biology Magazine.
[85] Jean-Baptiste Poline,et al. Bayesian estimation of the hemodynamic response function in functional MRI , 2002 .
[86] Xiaoshan Li,et al. Tucker Tensor Regression and Neuroimaging Analysis , 2018, Statistics in Biosciences.
[87] H. Benali,et al. Robust Bayesian estimation of the hemodynamic response function in event‐related BOLD fMRI using basic physiological information , 2003, Human brain mapping.
[88] Tingting Zhang,et al. A semi-parametric nonlinear model for event-related fMRI , 2014, NeuroImage.
[89] Stephen M Smith,et al. Fast robust automated brain extraction , 2002, Human brain mapping.
[90] Hongtu Zhu,et al. Tensor Regression with Applications in Neuroimaging Data Analysis , 2012, Journal of the American Statistical Association.
[91] Peter D. Hoff,et al. MULTILINEAR TENSOR REGRESSION FOR LONGITUDINAL RELATIONAL DATA. , 2014, The annals of applied statistics.
[92] T. Egner,et al. Emotional processing in anterior cingulate and medial prefrontal cortex , 2011, Trends in Cognitive Sciences.
[93] S. Kosslyn,et al. Impact of fMRI Acoustic Noise on the Functional Anatomy of Visual Mental Imagery , 2002, Journal of Cognitive Neuroscience.
[94] M. D’Esposito,et al. The variability of human BOLD hemodynamic responses , 1998, NeuroImage.
[95] Karl J. Friston,et al. Analysis of fMRI Time-Series Revisited—Again , 1995, NeuroImage.
[96] C. F. Beckmann,et al. Tensorial extensions of independent component analysis for multisubject FMRI analysis , 2005, NeuroImage.
[97] Martin A. Lindquist,et al. A hierarchical model for simultaneous detection and estimation in multi-subject fMRI studies , 2014, NeuroImage.
[98] Steffen Rendle,et al. Factorization Machines with libFM , 2012, TIST.
[99] Lars Kai Hansen,et al. Modeling the hemodynamic response in fMRI using smooth FIR filters , 2000, IEEE Transactions on Medical Imaging.
[100] Brian W. Haas,et al. Aberrant neurocognitive processing of fear in young girls with Turner syndrome. , 2014, Social cognitive and affective neuroscience.
[101] John Dunagan,et al. BLR-D: applying bilinear logistic regression to factored diagnosis problems , 2011 .