Influence of Individual Differences in fMRI-Based Pain Prediction Models on Between-Individual Prediction Performance

Decoding subjective pain perception from functional magnetic resonance imaging (fMRI) data using machine learning technique is gaining a growing interest. Despite the well-documented individual differences in pain experience and brain responses, it still remains unclear how and to what extent these individual differences affect the performance of between-individual fMRI-based pain prediction. The present study is aimed to examine the relationship between individual differences in pain prediction models and between-individual prediction error, and, further, to identify brain regions that contribute to between-individual prediction error. To this end, we collected and analyzed fMRI data and pain ratings in a laser-evoked pain experiment. By correlating different types of individual difference metrics with between-individual prediction error, we are able to quantify the influence of these individual differences on prediction performance and reveal a set of brain regions whose activities are related to prediction error. Interestingly, we found that the precuneus, which does not have predictive capability to pain, could also affect the prediction error. This study elucidates the influence of interindividual variability in pain on the between-individual prediction performance, and the results will be useful for the design of more accurate and robust fMRI-based pain prediction models.

[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 .