Influence of Individual Differences in fMRI-Based Pain Prediction Models on Between-Individual Prediction Performance
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Weixiang Liu | Qianqian Lin | Gan Huang | Linling Li | Zhiguo Zhang | Qianqian Lin | Linling Li | Jia Liu | Weixiang Liu | G. Huang | Zhiguo Zhang | Jia Liu
[1] Randy L. Gollub,et al. Exploring the brain in pain: Activations, deactivations and their relation , 2010, PAIN.
[2] Y. Hochberg. A sharper Bonferroni procedure for multiple tests of significance , 1988 .
[3] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[4] Mohammad Reza Daliri,et al. Decoding Objects of Basic Categories from Electroencephalographic Signals Using Wavelet Transform and Support Vector Machines , 2014, Brain Topography.
[5] Gian Domenico Iannetti,et al. Single-trial time–frequency analysis of electrocortical signals: Baseline correction and beyond , 2014, NeuroImage.
[6] Yeung Sam Hung,et al. A novel and effective fMRI decoding approach based on sliced inverse regression and its application to pain prediction , 2018, Neurocomputing.
[7] A. Bhattacharyya. On a measure of divergence between two statistical populations defined by their probability distributions , 1943 .
[8] Hermann Haken,et al. Exploring the Brain , 2013 .
[9] R. Treede,et al. The Kyoto protocol of IASP Basic Pain Terminology , 2008, PAIN®.
[10] M. Ingvar. Pain and functional imaging. , 1999, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.
[11] Bin He,et al. Quantifying and Characterizing Tonic Thermal Pain Across Subjects From EEG Data Using Random Forest Models , 2017, IEEE Transactions on Biomedical Engineering.
[12] Kevin Whittingstall,et al. Individual Differences in Pain Sensitivity Vary as a Function of Precuneus Reactivity , 2013, Brain Topography.
[13] Paul J Laurienti,et al. The subjective experience of pain: where expectations become reality. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[14] Joachim M. Buhmann,et al. Decoding the perception of pain from fMRI using multivariate pattern analysis , 2012, NeuroImage.
[15] A. Mouraux,et al. Determinants of laser-evoked EEG responses: pain perception or stimulus saliency? , 2008, Journal of neurophysiology.
[16] Yeung Sam Hung,et al. Decoding Subjective Intensity of Nociceptive Pain from Pre-stimulus and Post-stimulus Brain Activities , 2016, Front. Comput. Neurosci..
[17] D. Yarnitsky,et al. Pain sensitivity is inversely related to regional grey matter density in the brain , 2014, PAIN®.
[18] Martin A. Lindquist,et al. Group-regularized individual prediction: theory and application to pain , 2017, NeuroImage.
[19] S. M. Ali,et al. A General Class of Coefficients of Divergence of One Distribution from Another , 1966 .
[20] Roland Staud,et al. Individual differences in pain sensitivity: measurement, causation, and consequences. , 2009, The journal of pain : official journal of the American Pain Society.
[21] A. Mouraux,et al. The pain matrix reloaded A salience detection system for the body , 2011, Progress in Neurobiology.
[22] Katja Wiech,et al. Deconstructing the sensation of pain: The influence of cognitive processes on pain perception , 2016, Science.
[23] Eduardo García España. Terminology , 1973, Hybrid Nuclear Energy Systems.
[24] Michael J. Brammer,et al. Bayesian multi-task learning for decoding multi-subject neuroimaging data , 2014, NeuroImage.
[25] S. D. Jong. SIMPLS: an alternative approach to partial least squares regression , 1993 .
[26] Claudia Plant,et al. Decoding an individual's sensitivity to pain from the multivariate analysis of EEG data. , 2012, Cerebral cortex.
[27] M. Lindquist,et al. An fMRI-based neurologic signature of physical pain. , 2013, The New England journal of medicine.
[28] Yeung Sam Hung,et al. Normalization of Pain-Evoked Neural Responses Using Spontaneous EEG Improves the Performance of EEG-Based Cross-Individual Pain Prediction , 2016, Front. Comput. Neurosci..
[29] S. Mackey,et al. Towards a Physiology-Based Measure of Pain: Patterns of Human Brain Activity Distinguish Painful from Non-Painful Thermal Stimulation , 2011, PloS one.
[30] Martin A. Lindquist,et al. Brain mediators of the effects of noxious heat on pain , 2014, PAIN®.
[31] Marianne C. Reddan,et al. Modeling Pain Using fMRI: From Regions to Biomarkers , 2018, Neuroscience Bulletin.
[32] A. Mouraux,et al. From the neuromatrix to the pain matrix (and back) , 2010, Experimental Brain Research.
[33] P Baraldi,et al. Temporal and intensity coding of pain in human cortex. , 1998, Journal of neurophysiology.
[34] Janaina Mourão Miranda,et al. Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes , 2010, NeuroImage.
[35] T. Kinnunen,et al. Using Discrete Probabilities With Bhattacharyya Measure for SVM-Based Speaker Verification , 2011, IEEE Transactions on Audio, Speech, and Language Processing.
[36] Martin A Lindquist,et al. Quantifying cerebral contributions to pain beyond nociception , 2017, Nature Communications.
[37] E. Parzen. On Estimation of a Probability Density Function and Mode , 1962 .
[38] K. Davis,et al. The dynamic pain connectome , 2015, Trends in Neurosciences.
[39] Gian Domenico Iannetti,et al. Painful Issues in Pain Prediction , 2016, Trends in Neurosciences.
[40] Gian Domenico Iannetti,et al. A novel approach to predict subjective pain perception from single-trial laser-evoked potentials , 2013, NeuroImage.
[41] Fahim Mannan,et al. Interactive Image Segmentation , 2022 .