Neural oscillations and connectivity characterizing the state of tonic experimental pain in humans

Pain is a complex phenomenon that is served by neural oscillations and connectivity involving different brain areas and frequencies. Here, we aimed to systematically and comprehensively assess the pattern of neural oscillations and connectivity characterizing the state of tonic experimental pain in humans. To this end, we applied 10‐min heat pain stimuli consecutively to the right and left hand of 39 healthy participants and recorded electroencephalography. We systematically analyzed global and local measures of oscillatory brain activity, connectivity, and graph theory‐based network measures during tonic pain and compared them to a nonpainful control condition. Local measures showed suppressions of oscillatory activity at alpha frequencies together with stronger connectivity at alpha and beta frequencies in sensorimotor areas during tonic pain. Furthermore, sensorimotor areas contralateral to stimulation showed significantly increased connectivity to a common area in the medial prefrontal cortex at alpha frequencies. Together, these observations indicate that the state of tonic experimental pain is associated with a sensorimotor‐prefrontal network connected at alpha frequencies. These findings represent a step further toward understanding the brain mechanisms underlying long‐lasting pain states in health and disease.

[1]  Kenji Leibnitz,et al.  Classification and characterisation of brain network changes in chronic back pain: A multicenter study , 2017, bioRxiv.

[2]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[3]  T. Sejnowski,et al.  Removing electroencephalographic artifacts by blind source separation. , 2000, Psychophysiology.

[4]  A. Engel,et al.  Spectral fingerprints of large-scale neuronal interactions , 2012, Nature Reviews Neuroscience.

[5]  Ingo Bojak,et al.  When Long-Range Zero-Lag Synchronization is Feasible in Cortical Networks , 2012, Front. Comput. Neurosci..

[6]  Luiz A. Baccalá,et al.  Partial directed coherence: a new concept in neural structure determination , 2001, Biological Cybernetics.

[7]  Yong Hu,et al.  Changes of Spontaneous Oscillatory Activity to Tonic Heat Pain , 2014, PloS one.

[8]  P. Fries.  Rhythms for Cognition: Communication through Coherence , 2015, Neuron.

[9]  Joachim Gross,et al.  Brain oscillations differentially encode noxious stimulus intensity and pain intensity , 2017, NeuroImage.

[10]  André Mouraux,et al.  EEG frequency tagging using ultra-slow periodic heat stimulation of the skin reveals cortical activity specifically related to C fiber thermonociceptors , 2017, NeuroImage.

[11]  Philipp Berens,et al.  CircStat: AMATLABToolbox for Circular Statistics , 2009, Journal of Statistical Software.

[12]  H. Bastuji,et al.  Evidence‐based source modeling of nociceptive cortical responses: A direct comparison of scalp and intracranial activity in humans , 2017, Human brain mapping.

[13]  B. Seymour,et al.  Pain: A Distributed Brain Information Network? , 2015, PLoS biology.

[14]  D. Seminowicz,et al.  The Dorsolateral Prefrontal Cortex in Acute and Chronic Pain. , 2017, The journal of pain : official journal of the American Pain Society.

[15]  Joachim Gross,et al.  Prefrontal Gamma Oscillations Encode Tonic Pain in Humans , 2015, Cerebral cortex.

[16]  M. Siegel,et al.  A framework for local cortical oscillation patterns , 2011, Trends in Cognitive Sciences.

[17]  J. Schoffelen,et al.  Source connectivity analysis with MEG and EEG , 2009, Human brain mapping.

[18]  Olaf Sporns,et al.  Mechanisms of Zero-Lag Synchronization in Cortical Motifs , 2013, PLoS Comput. Biol..

[19]  Irene Tracey,et al.  Functional connectivity and pain: How effectively connected is your brain?☆ ☆ Please see related article in the June issue of PAIN: Mayer et al., PAIN 115/3, pp. 398–409. , 2005, Pain.

[20]  K. Davis,et al.  The dynamic pain connectome , 2015, Trends in Neurosciences.

[21]  J. Miró,et al.  Neuromodulatory treatments for chronic pain: efficacy and mechanisms , 2014, Nature Reviews Neurology.

[22]  H. Kennedy,et al.  Alpha-Beta and Gamma Rhythms Subserve Feedback and Feedforward Influences among Human Visual Cortical Areas , 2016, Neuron.

[23]  Patrick Dupont,et al.  Generalizable Representations of Pain, Cognitive Control, and Negative Emotion in Medial Frontal Cortex , 2017, Nature Neuroscience.

[24]  Herta Flor,et al.  Structural plasticity and reorganisation in chronic pain , 2016, Nature Reviews Neuroscience.

[25]  David A. Seminowicz,et al.  High frequency repetitive transcranial magnetic stimulation to the left dorsolateral prefrontal cortex modulates sensorimotor cortex function in the transition to sustained muscle pain , 2019, NeuroImage.

[26]  W. Singer,et al.  Visuomotor integration is associated with zero time-lag synchronization among cortical areas , 1997, Nature.

[27]  Cornelis J. Stam,et al.  Selective impairment of hippocampus and posterior hub areas in Alzheimer’s disease: an MEG-based multiplex network study , 2017, Brain : a journal of neurology.

[28]  J. Gross,et al.  Brain Rhythms of Pain , 2017, Trends in Cognitive Sciences.

[29]  Moritz M. Nickel,et al.  Autonomic responses to tonic pain are more closely related to stimulus intensity than to pain intensity , 2017, Pain.

[30]  J. Price,et al.  Sensory and premotor connections of the orbital and medial prefrontal cortex of macaque monkeys , 1995, The Journal of comparative neurology.

[31]  D. Vernon,et al.  Interpreting EEG alpha activity , 2014, Neuroscience & Biobehavioral Reviews.

[32]  Edward T. Bullmore,et al.  Efficiency and Cost of Economical Brain Functional Networks , 2007, PLoS Comput. Biol..

[33]  R. Oostenveld,et al.  Nonparametric statistical testing of EEG- and MEG-data , 2007, Journal of Neuroscience Methods.

[34]  Robert Oostenveld,et al.  An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias , 2011, NeuroImage.

[36]  D. Falla,et al.  The cortical responses to evoked clinical pain in patients with hip osteoarthritis , 2017, PloS one.

[37]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[38]  Abbas Sohrabpour,et al.  Spectral and spatial changes of brain rhythmic activity in response to the sustained thermal pain stimulation , 2016, Human brain mapping.

[39]  Saskia Haegens,et al.  Inter- and intra-individual variability in alpha peak frequency , 2014, NeuroImage.

[40]  C. Graversen,et al.  Dynamic spectral indices of the electroencephalogram provide new insights into tonic pain , 2015, Clinical Neurophysiology.

[41]  Wolfgang Klimesch,et al.  State-dependent alpha peak frequency shifts: Experimental evidence, potential mechanisms and functional implications , 2017, Neuroscience.

[42]  Leonardo L. Gollo,et al.  Dynamical relaying can yield zero time lag neuronal synchrony despite long conduction delays , 2008, Proceedings of the National Academy of Sciences.

[43]  D. Chialvo,et al.  Chronic Pain and the Emotional Brain: Specific Brain Activity Associated with Spontaneous Fluctuations of Intensity of Chronic Back Pain , 2006, The Journal of Neuroscience.

[44]  Alon Sinai,et al.  Tonic pain and continuous EEG: Prediction of subjective pain perception by alpha-1 power during stimulation and at rest , 2012, Clinical Neurophysiology.

[45]  André Mouraux,et al.  Tonic thermonociceptive stimulation selectively modulates ongoing neural oscillations in the human posterior insula: Evidence from intracerebral EEG , 2019, NeuroImage.

[46]  Jesper Andersson,et al.  Valid conjunction inference with the minimum statistic , 2005, NeuroImage.

[47]  A. Vania Apkarian,et al.  Nociception, Pain, Negative Moods, and Behavior Selection , 2015, Neuron.

[48]  A. Mouraux,et al.  The pain matrix reloaded A salience detection system for the body , 2011, Progress in Neurobiology.

[49]  C. Stam,et al.  Small-world networks and functional connectivity in Alzheimer's disease. , 2006, Cerebral cortex.

[50]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[51]  W. Klimesch.  Alpha-band oscillations, attention, and controlled access to stored information , 2012, Trends in Cognitive Sciences.

[52]  M. Baliki,et al.  Global disruption of degree rank order: a hallmark of chronic pain , 2016, Scientific Reports.

[53]  W. Drongelen,et al.  Localization of brain electrical activity via linearly constrained minimum variance spatial filtering , 1997, IEEE Transactions on Biomedical Engineering.

[54]  Jan-Mathijs Schoffelen,et al.  A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls , 2016, Front. Syst. Neurosci..

[55]  Sooyoung Yoo,et al.  Cerebral peak alpha frequency predicts individual differences in pain sensitivity , 2018, NeuroImage.

[56]  T LEHOCZKY,et al.  The pain , 1945, Fogorvosi szemle.

[57]  Uri T Eden,et al.  Emergence of Persistent Networks in Long-Term Intracranial EEG Recordings , 2011, The Journal of Neuroscience.

[58]  J. Palva,et al.  Discovering oscillatory interaction networks with M/EEG: challenges and breakthroughs , 2012, Trends in Cognitive Sciences.

[59]  Carl Y. Saab,et al.  Electroencephalographic frontal synchrony and caudal asynchrony during painful hand immersion in cold water , 2017, Brain Research Bulletin.

[60]  Pascal Tétreault,et al.  Corticolimbic anatomical characteristics predetermine risk for chronic pain. , 2016, Brain : a journal of neurology.

[61]  Herta Flor,et al.  Structural plasticity and reorganisation in chronic pain , 2017, Nature Reviews Neuroscience.

[62]  C. Stam.  Modern network science of neurological disorders , 2014, Nature Reviews Neuroscience.

[63]  Alon Sinai,et al.  Pain assessment by continuous EEG: Association between subjective perception of tonic pain and peak frequency of alpha oscillations during stimulation and at rest , 2010, Brain Research.

[64]  R. Peyron,et al.  Pain matrices and neuropathic pain matrices: A review , 2013, PAIN®.

[65]  J. Price.  Definition of the Orbital Cortex in Relation to Specific Connections with Limbic and Visceral Structures and Other Cortical Regions , 2007, Annals of the New York Academy of Sciences.

[66]  F. Balg,et al.  Unravelling the effect of experimental pain on the corticomotor system using transcranial magnetic stimulation and electroencephalography , 2017, Experimental Brain Research.

[67]  T. Schnitzer,et al.  Shape shifting pain: chronification of back pain shifts brain representation from nociceptive to emotional circuits. , 2013, Brain : a journal of neurology.

[68]  F. Varela,et al.  Measuring phase synchrony in brain signals , 1999, Human brain mapping.

[69]  Shivakumar Viswanathan,et al.  Frequency-specific modulation of connectivity in the ipsilateral sensorimotor cortex by different forms of movement initiation , 2017, NeuroImage.

[70]  O. Sporns,et al.  The economy of brain network organization , 2012, Nature Reviews Neuroscience.

[71]  Miriam Kunz,et al.  Responses to tonic heat pain in the ongoing EEG under conditions of controlled attention , 2014, Somatosensory & motor research.

[72]  M. Nitsche,et al.  Studying and modifying brain function with non-invasive brain stimulation , 2018, Nature Neuroscience.

[73]  M. Kramer,et al.  Emergence of Stable Functional Networks in Long-Term Human Electroencephalography , 2012, The Journal of Neuroscience.

[74]  Nakae,et al.  Classification and characterisation of brain network , 2020 .