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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Neurosci.</journal-id>
<journal-title>Frontiers in Neuroscience</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Neurosci.</abbrev-journal-title>
<issn pub-type="epub">1662-453X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fnins.2020.00147</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Neuroscience</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Linking Pain Sensation to the Autonomic Nervous System: The Role of the Anterior Cingulate and Periaqueductal Gray Resting-State Networks</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name><surname>Hohenschurz-Schmidt</surname> <given-names>David Johannes</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/792513/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Calcagnini</surname> <given-names>Giovanni</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Dipasquale</surname> <given-names>Ottavia</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/173942/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Jackson</surname> <given-names>Jade B.</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Medina</surname> <given-names>Sonia</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>O&#x2019;Daly</surname> <given-names>Owen</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>O&#x2019;Muircheartaigh</surname> <given-names>Jonathan</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff5"><sup>5</sup></xref>
<xref ref-type="aff" rid="aff6"><sup>6</sup></xref>
<xref ref-type="aff" rid="aff7"><sup>7</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/721640/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>de Lara Rubio</surname> <given-names>Alfonso</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/801521/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Williams</surname> <given-names>Steven C. R.</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>McMahon</surname> <given-names>Stephen B.</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name><surname>Makovac</surname> <given-names>Elena</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="corresp" rid="c002"><sup>&#x002A;</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/474708/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Howard</surname> <given-names>Matthew A.</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/617916/overview"/>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>Department of Neuroimaging, King&#x2019;s College London</institution>, <addr-line>London</addr-line>, <country>United Kingdom</country></aff>
<aff id="aff2"><sup>2</sup><institution>Department of Surgery and Cancer, Imperial College London</institution>, <addr-line>London</addr-line>, <country>United Kingdom</country></aff>
<aff id="aff3"><sup>3</sup><institution>Department of Technology and Health, Italian National Institute of Health</institution>, <addr-line>Rome</addr-line>, <country>Italy</country></aff>
<aff id="aff4"><sup>4</sup><institution>Wolfson Centre for Age Related Diseases, King&#x2019;s College London</institution>, <addr-line>London</addr-line>, <country>United Kingdom</country></aff>
<aff id="aff5"><sup>5</sup><institution>Sackler Institute for Translational Neurodevelopment, King&#x2019;s College London</institution>, <addr-line>London</addr-line>, <country>United Kingdom</country></aff>
<aff id="aff6"><sup>6</sup><institution>Centre for the Developing Brain, King&#x2019;s College London</institution>, <addr-line>London</addr-line>, <country>United Kingdom</country></aff>
<aff id="aff7"><sup>7</sup><institution>MRC Centre for Neurodevelopmental Disorders, King&#x2019;s College London</institution>, <addr-line>London</addr-line>, <country>United Kingdom</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Luke Henderson, The University of Sydney, Australia</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Giris Jacob, Tel Aviv Sourasky Medical Center, Israel; Vaughan G. Macefield, Baker Heart and Diabetes Institute, Australia</p></fn>
<corresp id="c001">&#x002A;Correspondence: David Johannes Hohenschurz-Schmidt, <email>d.hohenschurz-schmidt19@imperial.ac.uk</email></corresp>
<corresp id="c002">Elena Makovac, <email>elena.makovac@kcl.ac.uk</email></corresp>
<fn fn-type="other" id="fn004"><p>This article was submitted to Autonomic Neuroscience, a section of the journal Frontiers in Neuroscience</p></fn>
</author-notes>
<pub-date pub-type="epub">
<day>27</day>
<month>02</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="collection">
<year>2020</year>
</pub-date>
<volume>14</volume>
<elocation-id>147</elocation-id>
<history>
<date date-type="received">
<day>22</day>
<month>08</month>
<year>2019</year>
</date>
<date date-type="accepted">
<day>06</day>
<month>02</month>
<year>2020</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2020 Hohenschurz-Schmidt, Calcagnini, Dipasquale, Jackson, Medina, O&#x2019;Daly, O&#x2019;Muircheartaigh, de Lara Rubio, Williams, McMahon, Makovac and Howard.</copyright-statement>
<copyright-year>2020</copyright-year>
<copyright-holder>Hohenschurz-Schmidt, Calcagnini, Dipasquale, Jackson, Medina, O&#x2019;Daly, O&#x2019;Muircheartaigh, de Lara Rubio, Williams, McMahon, Makovac and Howard</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p></license>
</permissions>
<abstract>
<p>There are bi-directional interactions between the autonomic nervous system (ANS) and pain. This is likely underpinned by a substantial overlap between brain areas of the central autonomic network and areas involved in pain processing and modulation. To date, however, relatively little is known about the neuronal substrates of the ANS-pain association. Here, we acquired resting state fMRI scans in 21 healthy subjects at rest and during tonic noxious cold stimulation. As indicators of autonomic function, we examined how heart rate variability (HRV) frequency measures were influenced by tonic noxious stimulation and how these variables related to participants&#x2019; pain perception and to brain functional connectivity in regions known to play a role in both ANS regulation and pain perception, namely the right dorsal anterior cingulate cortex (dACC) and periaqueductal gray (PAG). Our findings support a role of the cardiac ANS in brain connectivity during pain, linking functional connections of the dACC and PAG with measurements of low frequency (LF)-HRV. In particular, we identified a three-way relationship between the ANS, cortical brain networks known to underpin pain processing, and participants&#x2019; subjectively reported pain experiences. LF-HRV both at rest and during pain correlated with functional connectivity between the seed regions and other cortical areas including the right dorsolateral prefrontal cortex (dlPFC), left anterior insula (AI), and the precuneus. Our findings link cardiovascular autonomic parameters to brain activity changes involved in the elaboration of nociceptive information, thus beginning to elucidate underlying brain mechanisms associated with the reciprocal relationship between autonomic and pain-related systems.</p>
</abstract>
<kwd-group>
<kwd>pain</kwd>
<kwd>autonomic nervous system</kwd>
<kwd>heart rate variability</kwd>
<kwd>fMRI</kwd>
<kwd>resting state</kwd>
<kwd>periaqueductal gray</kwd>
<kwd>anterior cingulate cortex</kwd>
</kwd-group>
<contract-num rid="cn001">MR/N026063/1</contract-num>
<contract-num rid="cn002">206675/Z/17/Z</contract-num>
<contract-sponsor id="cn001">Medical Research Council<named-content content-type="fundref-id">10.13039/501100000265</named-content></contract-sponsor>
<contract-sponsor id="cn002">Wellcome Trust<named-content content-type="fundref-id">10.13039/100004440</named-content></contract-sponsor>
<counts>
<fig-count count="3"/>
<table-count count="3"/>
<equation-count count="0"/>
<ref-count count="147"/>
<page-count count="15"/>
<word-count count="0"/>
</counts>
</article-meta>
</front>
<body>
<sec id="S1">
<title>Introduction</title>
<p>Neural networks involved in pain processing are intimately linked to the autonomic nervous system (ANS) (<xref ref-type="bibr" rid="B16">Benarroch, 2006</xref>): On the one hand, the body&#x2019;s response to pain is defined by changes in ANS parameters (<xref ref-type="bibr" rid="B69">Kyle and McNeil, 2014</xref>); on the other, alterations in autonomic arousal can also influence the experience of pain (<xref ref-type="bibr" rid="B127">Terkelsen et al., 2004</xref>). There is growing interest in mindfulness-based and other mind-body interventions in the treatment and management of pain (<xref ref-type="bibr" rid="B117">Stanos, 2012</xref>; <xref ref-type="bibr" rid="B46">Goyal et al., 2014</xref>), with changes in autonomic balance one of the likely underlying mechanisms of action (<xref ref-type="bibr" rid="B124">Tang et al., 2015</xref>). Whilst various regions of the central nervous system (CNS) are known to play a role in both pain and the ANS, currently there is a lack of knowledge concerning how pain-autonomic interactions may be reflected by functional connections in the brain.</p>
<p>One possible mechanism underlying this pain-autonomic interaction is the baroreflex, the negative feedback loop used to maintain stable blood-pressure (<xref ref-type="bibr" rid="B122">Suarez-Roca et al., 2018</xref>). This mechanism has been associated with observed reduction in pain perception in healthy controls during spontaneous high blood pressure (during which baroreceptors are activated) and also during mechanical stimulation of baroreceptors (<xref ref-type="bibr" rid="B38">Edwards et al., 2001</xref>; <xref ref-type="bibr" rid="B37">Duschek et al., 2007</xref>; <xref ref-type="bibr" rid="B103">Reyes del Paso et al., 2014</xref>). Decreased baroreceptor sensitivity has also been described in some chronic pain conditions (<xref ref-type="bibr" rid="B34">Davydov et al., 2018</xref>).</p>
<p>Heart rate variability (HRV), which is derived from variations in interval length between consecutive heart beats (<xref ref-type="bibr" rid="B126">Task Force, 1996</xref>; <xref ref-type="bibr" rid="B18">Berntson et al., 1997</xref>; <xref ref-type="bibr" rid="B129">Thayer et al., 2010a</xref>, <xref ref-type="bibr" rid="B128">2012</xref>), is often estimated to assess the autonomic response to experimental pain (<xref ref-type="bibr" rid="B61">Koenig et al., 2014</xref>). Low frequency (LF)-HRV spectral power is thought to represent the baroreflex-dependent outflow to the heart, whereas high frequency (HF) is interpreted as indicator of vagal cardiac control, and the ratio between the two measures (LF/HF) as a reflection of sympathovagal balance (<xref ref-type="bibr" rid="B93">Pagani et al., 1986</xref>, <xref ref-type="bibr" rid="B94">2012</xref>; <xref ref-type="bibr" rid="B82">Montano et al., 2009</xref>; <xref ref-type="bibr" rid="B129">Thayer et al., 2010a</xref>; <xref ref-type="bibr" rid="B45">Goldstein et al., 2011</xref>; <xref ref-type="bibr" rid="B102">Reyes del Paso et al., 2013</xref>). Experimentally induced pain increases LF power and the LF/HF ratio (<xref ref-type="bibr" rid="B61">Koenig et al., 2014</xref>), indicating an increased engagement of the baroreflex. Few studies have employed tonic cold pain stimuli, but results from cold water hand immersion tests (i.e., the cold pressor test) point toward a similar tendency of increased LF-HRV (<xref ref-type="bibr" rid="B84">Mourot et al., 2009</xref>; <xref ref-type="bibr" rid="B119">Streff et al., 2010</xref>).</p>
<p>A pain-suppressive effect associated with larger HF spectral power at baseline has been found (<xref ref-type="bibr" rid="B86">Nahman-Averbuch et al., 2016</xref>; <xref ref-type="bibr" rid="B133">Tracy et al., 2018</xref>), which might be explained by superior capacity to engage vagal cardiac control (<xref ref-type="bibr" rid="B101">Reyes del Paso et al., 2011</xref>; <xref ref-type="bibr" rid="B26">Busch et al., 2013</xref>; <xref ref-type="bibr" rid="B146">Zunhammer et al., 2013</xref>; <xref ref-type="bibr" rid="B133">Tracy et al., 2018</xref>). Conversely, higher resting LF-HRV has been found to predict reduced thermal pain sensitivity (<xref ref-type="bibr" rid="B7">Appelhans and Luecken, 2008</xref>; <xref ref-type="bibr" rid="B133">Tracy et al., 2018</xref>). Patients with chronic pain conditions such as fibromyalgia often show reduced HF power in addition to increased LF and LF/HF, suggesting dysregulated autonomic cardiac control (<xref ref-type="bibr" rid="B79">Meeus et al., 2013</xref>) and altered baroreflex engagement (<xref ref-type="bibr" rid="B25">Bruehl et al., 2017</xref>). In brief, HRV indices respond to noxious stimuli and relate to the subjective intensity of pain, but can also predict the experience of pain, indicating a bi-directional association where nociception influences the ANS and, conversely, the ANS modulates the experience of pain.</p>
<p>Modern neuroimaging techniques, such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), have been used to investigate the brain regions associated with autonomic activity during various tasks and conditions. Significant correlations between HRV, amygdala and medial prefrontal cortex (mPFC) activity have been demonstrated (<xref ref-type="bibr" rid="B128">Thayer et al., 2012</xref>; <xref ref-type="bibr" rid="B13">Beissner et al., 2013</xref>; <xref ref-type="bibr" rid="B118">Steinfurth et al., 2018</xref>). In resting-state fMRI (rs-fMRI) paradigms, HRV measures were associated with functional connectivity of different resting state networks (RSNs). For example, <xref ref-type="bibr" rid="B56">Jennings et al. (2016)</xref> found participants&#x2019; HF-HRV at rest to correlate with resting state connectivity of the mPFC, but not with salience (SN) or default mode networks (DMN). Functional connectivity of the dorsal anterior cingulate cortex (dACC) to the thalamus and brainstem co-varies with HF-HRV, whilst LF-HRV relates to dACC connectivity with the temporoparietal junction (<xref ref-type="bibr" rid="B30">Chang et al., 2013</xref>). Further, measures of vagal output have been shown to be associated with functional connectivity between cortical areas and parts of the brainstem (e.g., <xref ref-type="bibr" rid="B111">Smith et al., 2015</xref>; <xref ref-type="bibr" rid="B12">B&#x00E4;r et al., 2016</xref>).</p>
<p>Many brain regions involved in ANS activity are also active during the experience of pain, including the anterior cingulate cortex (ACC), amygdala, and periaqueductal gray (PAG) (<xref ref-type="bibr" rid="B72">Leone et al., 2006</xref>; <xref ref-type="bibr" rid="B48">Heinricher and Fields, 2013</xref>). The dACC is consistently found to be involved in the CNS response to noxious stimulation (<xref ref-type="bibr" rid="B136">Vogt et al., 2003</xref>; <xref ref-type="bibr" rid="B5">Apkarian et al., 2005</xref>; <xref ref-type="bibr" rid="B72">Leone et al., 2006</xref>; <xref ref-type="bibr" rid="B36">Duerden and Albanese, 2013</xref>; <xref ref-type="bibr" rid="B57">Jensen et al., 2016</xref>). Whilst the more rostral parts are associated with the affective component of pain, the most dorsal aspect (bordering the mid-cingulate cortex) encodes the objective aspects of pain (e.g., stimulus intensity) (<xref ref-type="bibr" rid="B100">Rainville et al., 1999</xref>; <xref ref-type="bibr" rid="B110">Singer et al., 2004</xref>; <xref ref-type="bibr" rid="B137">Wager et al., 2004</xref>; <xref ref-type="bibr" rid="B2">Amodio and Frith, 2006</xref>; <xref ref-type="bibr" rid="B20">Boccard et al., 2014</xref>). Taken together, the dACC forms part of a network of brain regions involved in the detection of salient sensory events, including the multimodal context-dependent experience of pain, but is also intimately linked to the ANS and the sensing of internal body states (i.e., interoception) (<xref ref-type="bibr" rid="B32">Craig, 2002</xref>; <xref ref-type="bibr" rid="B132">Tracey and Mantyh, 2007</xref>; <xref ref-type="bibr" rid="B71">Legrain et al., 2011</xref>). Similarly, the midbrain PAG receives both peripheral nociceptive input and descending projections from the hypothalamus, amygdala, and rostral ACC. Providing output to medullary centers, the PAG is an essential component of a descending pain modulatory system that inhibits or facilitates nociceptive processing within the spinal dorsal horn (<xref ref-type="bibr" rid="B91">Ossipov et al., 2010</xref>; <xref ref-type="bibr" rid="B48">Heinricher and Fields, 2013</xref>). PAG neurons projecting to autonomic centers in the medulla are also involved in cardiovascular changes observed during opioid-dependent and independent endogenous analgesia (<xref ref-type="bibr" rid="B16">Benarroch, 2006</xref>; <xref ref-type="bibr" rid="B47">Green et al., 2006</xref>). In addition, stimulation of the PAG alters baroreflex sensitivity and cardiac control (<xref ref-type="bibr" rid="B96">Pelosi et al., 2007</xref>; <xref ref-type="bibr" rid="B97">Pereira et al., 2010</xref>; <xref ref-type="bibr" rid="B17">Benarroch, 2012</xref>; <xref ref-type="bibr" rid="B70">Lagatta et al., 2016</xref>).</p>
<p>Due to extensive functional overlap between structures involved in autonomic control, nociception, and pain sensation at different levels of the spinal cord, brainstem, midbrain, and cortex (<xref ref-type="bibr" rid="B15">Benarroch, 2001</xref>, <xref ref-type="bibr" rid="B16">2006</xref>), the reciprocal ability of the ANS to modulate nociceptive information is not surprising. Amongst others, emotional appraisal (<xref ref-type="bibr" rid="B141">Wiech and Tracey, 2009</xref>), attention (<xref ref-type="bibr" rid="B55">James and Hardardottir, 2002</xref>; <xref ref-type="bibr" rid="B23">Bradshaw et al., 2012</xref>), mood (<xref ref-type="bibr" rid="B28">Carter et al., 2002</xref>), hypnotic suggestion (<xref ref-type="bibr" rid="B100">Rainville et al., 1999</xref>), and stress (<xref ref-type="bibr" rid="B127">Terkelsen et al., 2004</xref>; <xref ref-type="bibr" rid="B11">Ballegaard et al., 2014</xref>) can influence the subjective pain experience. Any of these states corresponds with changes in ANS activity (<xref ref-type="bibr" rid="B140">Wiech et al., 2008</xref>; <xref ref-type="bibr" rid="B64">Kreibig, 2010</xref>) and baroreflex sensitivity (<xref ref-type="bibr" rid="B40">Fejes et al., 2020</xref>).</p>
<p>Whilst previous studies have described brain networks underlying pain-induced sympathetic reactions in response to tonic pain (<xref ref-type="bibr" rid="B59">Kobuch et al., 2017</xref>, <xref ref-type="bibr" rid="B60">2018</xref>), only one study so far has investigated how variations in HRV relate to brainstem functional connectivity changes observed during a prolonged painful experience: <xref ref-type="bibr" rid="B107">Sclocco et al. (2016)</xref> convolved HRV with the hemodynamic response function during 6 min of pressure pain, identifying several brainstem nuclei (specifically, rostral ventromedial medulla, ventral nucleus reticularis/nucleus ambiguous, and pontine nuclei) associated with pain-evoked HRV alterations. To date, the bi-directional association between pain-engaged higher brain networks (involving, for example, the anterior cingulate cortex) and HRV (either resting or in response to pain) remains unexplored.</p>
<p>In this study, we combined sampling of HRV and rs-fMRI, both during rest and tonic noxious cold stimulation, in a group of healthy participants. We adopted a hypothesis-driven region of interest (ROI) approach, drawing upon regions known to play important roles in both ANS function and nociceptive processing, namely the dACC and the PAG (<xref ref-type="bibr" rid="B16">Benarroch, 2006</xref>; <xref ref-type="bibr" rid="B72">Leone et al., 2006</xref>; <xref ref-type="bibr" rid="B132">Tracey and Mantyh, 2007</xref>; <xref ref-type="bibr" rid="B71">Legrain et al., 2011</xref>). Following previous studies, we hypothesized that HRV measures at baseline and during noxious stimulation will be associated with subjective pain intensity ratings. Further, we hypothesized that PAG and dACC RSNs would be affected by a tonic noxious stimulus, and that these pain-induced rs-fMRI changes would correlate with HRV parameters and subjective ratings of pain. Lastly, we explored whether baseline HRV is associated with the brain response to a noxious stimulus.</p>
</sec>
<sec id="S2" sec-type="materials|methods">
<title>Materials and Methods</title>
<sec id="S2.SS1">
<title>Participants</title>
<p>Twenty three healthy participants took part in the experiment. Two participants were discarded due to poor quality of the physiology data. The final sample comprised 21 participants (8 females, mean age: 26.1, SD: 5.2). Further, pain ratings were obtained from 17 of those participants only (five participants did not attend the final testing session). All participants were right-handed as assessed by the Edinburgh handedness inventory (<xref ref-type="bibr" rid="B90">Oldfield, 1971</xref>). In addition to MRI contraindications, exclusion criteria (verified by means of standardized questionnaires or semi-structured interview) included: a history of psychiatric illness; substance abuse [as verified by Sections 11 and 12 of the Schedules for Clinical Assessment in Neuropsychiatry (SCAN) (<xref ref-type="bibr" rid="B143">Wing et al., 1990</xref>); and by the Alcohol Use Disorders Identification Test (AUDIT) (<xref ref-type="bibr" rid="B50">Higgins-Biddle and Babor, 2018</xref>)]; chronic pain conditions; diagnosed medical or psychological conditions that might compromise participation in the study or interfere with somatosensation; cardiovascular medication and medication which might affect temperature sensitivity (e.g., tricyclic antidepressants). All female participants were tested within the follicular phase to reduce hormonal effects on HRV (<xref ref-type="bibr" rid="B104">Sato et al., 1995</xref>) and pain sensitivity (<xref ref-type="bibr" rid="B78">Martin, 2009</xref>; <xref ref-type="bibr" rid="B53">Iacovides et al., 2015</xref>).</p>
<p>To minimize the influence of diurnal variations on pain responses (<xref ref-type="bibr" rid="B120">Strian et al., 1989</xref>; <xref ref-type="bibr" rid="B51">Hodkinson et al., 2014</xref>), rs-fMRI networks activity (<xref ref-type="bibr" rid="B19">Blautzik et al., 2013</xref>; <xref ref-type="bibr" rid="B58">Jiang et al., 2016</xref>), and HRV (<xref ref-type="bibr" rid="B39">Ewing et al., 1991</xref>; <xref ref-type="bibr" rid="B73">Li et al., 2011</xref>; <xref ref-type="bibr" rid="B145">Xhyheri et al., 2012</xref>), participants were always tested at the same time of the day. At the beginning of each visit, participants were tested for drugs of abuse (urine drug test) and alcohol consumption (alcohol breathalyzer). Participants&#x2019; autonomic reactions (blood pressure and heart rate) were tested at the beginning and at the end of each experimental session, in a sitting and standing position, to identify any anomalous cardiovascular behavior. All participants provided written informed consent. The study was approved by the King&#x2019;s College Research Ethics Committee in accordance with the principles of the Helsinki declaration.</p>
</sec>
<sec id="S2.SS2">
<title>Experimental Procedure</title>
<p>Participants attended three visits: a first familiarization session, a scanning session, and a post-scanning session. During the familiarization session, participants were accustomed with the cold stimulation and the MRI environment.</p>
<p>During the scanning session, participants underwent three consecutive resting-state fMRI blocks, each of 6 min duration: a baseline resting condition (&#x201C;Baseline&#x201D;), and a prolonged noxious cold stimulation (&#x201C;Cold-pain&#x201D;) and a post-cold recovery session. For this study, data from the first two blocks were analyzed, whilst the results of the last block are presented in <xref ref-type="bibr" rid="B75">Makovac et al. (2019)</xref>. To elicit pain, 2&#x00B0;C cold-water was circulated via a custom-made aluminum thermode (4 &#x00D7; 20 cm) applied to the volar surface of the participants&#x2019; left forearm, via a high capacity (700W) solid state circulating chiller unit (Thermotek RC22A750) employed to deliver stable temperature control of the afferent stimulation over time. Following an initial stabilization period (&#x223C;20 s), a mean temperature was maintained at 2.5&#x00B0;C (&#x00B1;0.9&#x00B0;C) throughout the experimental block.</p>
<p>During each resting state period, participants were instructed to rest with their eyes open, and focus on a fixation cross presented at the center of the screen, without thinking of anything and not falling asleep. Heart rate (HR) was sampled continuously during the Baseline and Cold-pain condition by means of photoplethysmography (PPG), using an inbuilt MRI-compatible pulse oximeter (General Electric) fitted to the participants&#x2019; right index finger. In stationary conditions, pulse oximetry has been shown to be a good surrogate measure for ECG-derived HRV (<xref ref-type="bibr" rid="B44">Gil et al., 2010</xref>; <xref ref-type="bibr" rid="B105">Sch&#x00E4;fer and Vagedes, 2013</xref>).</p>
<p>In our original experimental paradigm, the &#x201C;cold-pain&#x201D; resting state session was followed immediately by a further &#x201C;post-cold&#x201D; session (see <xref ref-type="bibr" rid="B75">Makovac et al., 2019</xref>), which precluded the provision of an intermediate subjective response to the prolonged noxious stimulation. To circumvent this, each participant&#x2019;s subjective experience of cold pain was explored in a further post-scanning session. Here, participants were instructed to rate the pain level and unpleasantness experienced during the same 6-min 2&#x00B0;C cold stimulation on a visual analog scale (VAS) ranging from 0 (no pain) to 100 (worst pain imaginable) (<xref ref-type="bibr" rid="B52">Howard et al., 2006</xref>; <xref ref-type="bibr" rid="B77">Marquand et al., 2010</xref>). Cold pain ratings were robust and reliable (see <xref ref-type="supplementary-material" rid="DS1">Supplementary Material S1</xref> for more details).</p>
</sec>
<sec id="S2.SS3">
<title>Heart Rate Variability</title>
<p>Photoplethysmography inter-beat interval (IBI) data were plotted in Matlab, visually inspected, and potential artifacts removed manually. IBI values were used as inputs into Kubios HRV Standard ver. 3.0.2 software (<xref ref-type="bibr" rid="B125">Tarvainen et al., 2014</xref>). Detrending was performed based on smoothness priors. Frequency domain measures were extracted into MS Excel and IBM SPSS Statistics for Macintosh (ver. 24) for statistical analysis. Data were then scanned for outliers using boxplots and exploratory statistics in SPSS. Values for HRV frequency bands are LF: 0.04&#x2013;0.15 Hz, and HF: 0.15&#x2013;0.4 Hz, as recommended by a 1996 task force publication on HRV (<xref ref-type="bibr" rid="B126">Task Force, 1996</xref>). The natural logarithms of LF and HF power (in ms<sup>2</sup>) were calculated, with the aim to reduce skewness and kurtosis of HRV parameters and to enable the data to more closely conform to the assumptions of normality. Other studies have reported that the log of LF power correlates positively with the log of baroreflex-cardiovagal gain (<xref ref-type="bibr" rid="B81">Moak et al., 2007</xref>; <xref ref-type="bibr" rid="B99">Rahman et al., 2011</xref>).</p>
<p>In order to differentiate early HRV alterations from later ANS habituation to noxious stimulation, HRV frequency measures were extracted from two separate intervals; Interval 1 (0&#x2013;3 min from the beginning of the session) and Interval 2 (3&#x2013;6 min), separately for the Baseline and Cold-pain session. HRV measures derived from a 3-min sample have good inter-session reliability (see <xref ref-type="supplementary-material" rid="DS1">Supplementary Material S3</xref>).</p>
</sec>
<sec id="S2.SS4">
<title>MRI Acquisition and Preprocessing</title>
<p>MR images were acquired on a 3T GE MR750 scanner, with a 32-channel receive-only head coil (NovaMedical). Structural volumes were obtained using the high-resolution three-dimensional magnetization-prepared rapid gradient-echo sequence (TR = 7312 ms, TE = 3.02 ms, flip angle = 11&#x00B0;, slice thickness = 1.2 mm, 196 sagittal slices, FOV = 270 mm). Functional datasets used T2<sup>&#x2217;</sup>weighted multi-echo imaging (EPI) sensitive to blood oxygenation level dependent (BOLD) signal (TR = 2 s, TE1 = 12 ms, TE2 = 28 ms; TE3 = 44 ms; flip-angle 80&#x00B0;, 32 slices, 3 mm slice thickness, 240 mm FOV, voxel size 3.75 &#x00D7; 3.75 &#x00D7; 3 mm). By acquiring multiple echo images per slice, multi-echo fMRI allows to identify non-BOLD related sources of signal and preserves the signal of interest (<xref ref-type="bibr" rid="B35">Dipasquale et al., 2017</xref>).</p>
<p>Pre-processing was performed using AFNI (<xref ref-type="bibr" rid="B31">Cox, 1996</xref>), the Advanced Normalization Tools (ANTs) (<xref ref-type="bibr" rid="B8">Avants et al., 2011</xref>), and FSL (<xref ref-type="bibr" rid="B113">Smith et al., 2004</xref>). Steps included volume re-alignment, time-series de-spiking and slice time correction. After pre-processing, functional data were optimally combined (OC) by taking a weighted summation of the three echoes using an exponential T2<sup>&#x2217;</sup> weighting approach (<xref ref-type="bibr" rid="B98">Posse et al., 1999</xref>). The OC data were then de-noised adopting a Multi-Echo ICA approach implemented by the tool meica. py (Version v2.5 beta9) (<xref ref-type="bibr" rid="B67">Kundu et al., 2013</xref>, <xref ref-type="bibr" rid="B68">2014</xref>), given its effectiveness in removing physiological and motion-related noise and increasing temporal SNR (<xref ref-type="bibr" rid="B67">Kundu et al., 2013</xref>; <xref ref-type="bibr" rid="B35">Dipasquale et al., 2017</xref>). Briefly, multi-echo principal component analysis was first used to reduce the data dimensionality in the OC dataset. Spatial ICA was then applied to one echo, and the independent component time-series were fitted to the pre-processed time-series from each of the three echoes to generate ICA weights for each echo. These weights were then fitted to the linear TE-dependence and TE-independence models to generate F-statistics and component-level &#x03BA; and &#x03C1; values, which, respectively, indicate BOLD and non-BOLD weightings. The &#x03C1; metrics were then used to identify non-BOLD-like components to be regressed out of the OC dataset as noise. For further technical details on multi-echo ICA refer to <xref ref-type="bibr" rid="B66">Kundu et al. (2015)</xref>.</p>
<p>Next, white matter and cerebrospinal fluid time-series were regressed out using FSL. A high-pass temporal filter with a cut-off frequency of 0.005 Hz was applied, and the data were spatially smoothed with a 5 mm FWHM Gaussian kernel. Each participant&#x2019;s dataset was co-registered to its corresponding structural scan using affine-only registration. Then, using a non-linear registration approach, functional data were normalized to standard MNI152 space and resampled to 2 &#x00D7; 2 &#x00D7; 2 mm<sup>3</sup> using ANTs.</p>
</sec>
<sec id="S2.SS5">
<title>Statistical Analyses</title>
<sec id="S2.SS5.SSS1">
<title>HRV Reactions to Pain</title>
<p>We tested for a difference in HRV parameters either with pain induction or between the initial and final interval of each test condition. A two-way within-subject ANOVA was used to explore the main effect of Condition (Baseline, Cold-pain), Interval (starting 3 min, final 3 min), and the Condition x Interval interaction, separately for LF-HRV and HF-HRV.</p>
<p>Next, we examined whether subjects&#x2019; pain sensitivity correlated with HRV at baseline and during cold stimulation. All data were expressed as means (&#x00B1;SD). Differences at <italic>p</italic> &#x003C; 0.05 were regarded as significant. Data analysis was performed with SPSS 23.0 for Windows (SPSS Inc., United States).</p>
</sec>
</sec>
<sec id="S2.SS6">
<title>Seed-Based fMRI Analysis</title>
<p>Anatomical ROIs were constructed using the Marsbar toolbox implemented in SPM 12<sup><xref ref-type="fn" rid="footnote1">1</xref></sup>. The cingulate-seed was located at MNI<sub>xyz</sub> = (2, 8, 38) (7 mm spherical radius; right dACC) and the PAG ROI at MNI<sub>xyz</sub> = (0, &#x2212;30, &#x2212;1) (3 mm spherical radius). Our ROIs were based on results from pre-existing data acquired by our group (<xref ref-type="bibr" rid="B75">Makovac et al., 2019</xref>) and previously published examples of the dACC (<xref ref-type="bibr" rid="B62">Kong et al., 2010a</xref>; <xref ref-type="bibr" rid="B36">Duerden and Albanese, 2013</xref>; <xref ref-type="bibr" rid="B142">Wilcox et al., 2015</xref>) and the PAG (<xref ref-type="bibr" rid="B63">Kong et al., 2010b</xref>; <xref ref-type="bibr" rid="B147">Zyloney et al., 2010</xref>; <xref ref-type="bibr" rid="B74">Mainero et al., 2011</xref>). Functionally, we chose these two regions because of their known involvement in both pain processing and ANS control (see <xref ref-type="supplementary-material" rid="DS1">Supplementary Material S4</xref> for a Neurosynth &#x2013; <ext-link ext-link-type="uri" xlink:href="https://neurosynth.org/">https://neurosynth.org/</ext-link> &#x2013; based meta-analysis of pain-related and ANS-related studies, S4). It is of note that, given the extension of the sphere used to build our ROI, the dACC ROI covered an area on the boundary between dACC and medial cingulate cortex (MCC), as defined by <xref ref-type="bibr" rid="B135">Vogt (2016)</xref>.</p>
<p>The average resting state fMRI time-series in each ROI was extracted for each participant and for each scan and used as a regressor at 1st level SPM analysis with the purpose of determining the voxels in the brain showing a significant correlation with each ROI. Next, group analyses were performed, in which participants&#x2019; first level contrast images for the Baseline and Cold-pain conditions were included in a paired <italic>t</italic>-test, to explore the network of areas positively associated with our seed regions in each condition.</p>
<p>We performed three different regression analyses, using HRV measures and subjective pain ratings as covariates of interest. First, we explored the relationship between our resting-state networks and pain-induced HRV alterations. We tested whether baseline dACC and PAG functional connectivity predicted HRV changes during pain, and whether dACC and PAG functional connectivity during cold pain was associated with HRV reactions to cold pain. Next, we investigated whether baseline dACC-PAG functional connectivity underlies the association between HRV and pain. Here, both HRV and subjective pain ratings were entered into the same general linear model. Lastly, to explore the modulatory role of the HRV toward pain perception, we tested whether baseline HRV measures could predict pain-related dACC-PAG RSN alterations.</p>
<p>Due to potential gender-specific differences in pain processing (<xref ref-type="bibr" rid="B83">Moulton et al., 2006</xref>; <xref ref-type="bibr" rid="B49">Henderson et al., 2008</xref>; <xref ref-type="bibr" rid="B95">Paller et al., 2009</xref>), gender was used as a covariate of no interest. Similarly, age has been shown to influence HRV in healthy individuals (<xref ref-type="bibr" rid="B89">O&#x2019;Brien et al., 1986</xref>; <xref ref-type="bibr" rid="B4">Antelmi et al., 2004</xref>) and was thus controlled for in our analyses. Statistical threshold was set to <italic>p</italic> &#x003C; 0.05 &#x2013; FWE-corrected at cluster level (cluster size defined using uncorrected voxel-level threshold <italic>p</italic> &#x003C; 0.005), according to Gaussian Random Field Theory (<xref ref-type="bibr" rid="B144">Worsley et al., 1992</xref>).</p>
</sec>
</sec>
<sec id="S3">
<title>Results</title>
<sec id="S3.SS1">
<title>Sample Characteristics</title>
<p><xref ref-type="table" rid="T1">Table 1</xref> provides a summary of the main demographic and baseline characteristics of our sample. After the 6-min cold stimulation, participants gave an average pain rating of 45.8 (SD = 22.2) on a 0&#x2013;100 VAS scale.</p>
<table-wrap position="float" id="T1">
<label>TABLE 1</label>
<caption><p>Sample characteristics and heart rate variability (HRV) measures.</p></caption>
<table cellspacing="5" cellpadding="5" frame="hsides" rules="groups">
<thead>
<tr>
<td valign="top" align="left"><bold>Total <italic>n</italic></bold></td>
<td valign="top" align="center"><bold>21</bold></td>
<td/>
<td valign="top" colspan="4"/>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><bold>Sample characteristic</bold></td>
<td valign="top" align="center"><bold>M</bold></td>
<td valign="top" align="center"><bold>SD</bold></td>
<td valign="top" align="left" colspan="2"><bold>HRV measure</bold></td>
<td valign="top" align="center"><bold>M</bold></td>
<td valign="top" align="center"><bold>SD</bold></td>
</tr>
<tr>
<td valign="top" align="left" colspan="3"><hr/></td>
<td valign="top" align="left" colspan="4"><hr/></td>
</tr>
<tr>
<td valign="top" align="left"><bold>age</bold></td>
<td valign="top" align="center">26.1</td>
<td valign="top" align="center">(&#x00B1;5.2)</td>
<td valign="top" align="left"><bold>Log LF</bold> (absolute) in ms<sup>2</sup></td>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left"><bold>gender ratio</bold></td>
<td/>
<td/>
<td/>
<td valign="top" align="left"><italic>BL</italic></td>
<td valign="top" align="center">2.913 (1461.12)</td>
<td valign="top" align="center">&#x00B1;0.405 (&#x00B1;1195.53)</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;<italic>female. n(%)</italic></td>
<td valign="top" align="center">8</td>
<td valign="top" align="center">38.1</td>
<td/>
<td valign="top" align="left"><italic>Cold-pain</italic></td>
<td valign="top" align="center">2.936 (1537.21)</td>
<td valign="top" align="center">&#x00B1;0.460 (&#x00B1;1686.35)</td>
</tr>
<tr>
<td valign="top" align="left"><bold>BMI</bold></td>
<td valign="top" align="center">22.1</td>
<td valign="top" align="center">(&#x00B1;2.4)</td>
<td valign="top" align="left"><bold>Log HF</bold> (absolute) in ms<sup>2</sup></td>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left"><bold>rSBP</bold> <italic>mnHg</italic></td>
<td valign="top" align="center">118.8</td>
<td valign="top" align="center">(&#x00B1;9.6)</td>
<td/>
<td valign="top" align="left"><italic>BL</italic></td>
<td valign="top" align="center">2.938 (1468.86)</td>
<td valign="top" align="center">&#x00B1;0.403 (&#x00B1;1577.27)</td>
</tr>
<tr>
<td valign="top" align="left"><bold>HRBL</bold> <italic>bpm</italic></td>
<td valign="top" align="center">52.7</td>
<td valign="top" align="center">(&#x00B1;10)</td>
<td/>
<td valign="top" align="left"><italic>Cold-pain</italic></td>
<td valign="top" align="center">3.022 (1713.5)</td>
<td valign="top" align="center">&#x00B1;0.382 (&#x00B1;1654.64)</td>
</tr>
<tr>
<td valign="top" align="left"><bold>HRCold-pain</bold> <italic>bpm</italic></td>
<td valign="top" align="center">50.8</td>
<td valign="top" align="center">(&#x00B1;9.9)</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left"><bold>VAS</bold> (<italic>n</italic> = 17)</td>
<td valign="top" align="center">45.8</td>
<td valign="top" align="center">(&#x00B1;22.2)</td>
<td valign="top" align="left"><bold>LF/HF</bold></td>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left"><bold>caffeine</bold> <italic>drinks p.d.</italic></td>
<td valign="top" align="center">1.7</td>
<td valign="top" align="center">(&#x00B1;1.3)</td>
<td/>
<td valign="top" align="left"><italic>BL</italic></td>
<td valign="top" align="center">&#x2212;0.0247</td>
<td valign="top" align="center">(&#x00B1;0.426)</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Cigarettes</bold> <italic>p.d.</italic></td>
<td valign="top" align="center">0.5</td>
<td valign="top" align="center">(&#x00B1;2.3)</td>
<td/>
<td valign="top" align="left"><italic>Cold-pain</italic></td>
<td valign="top" align="center">&#x2212;0.0863</td>
<td valign="top" align="center">(&#x00B1;0.333)</td>
</tr>
<tr>
<td valign="top" align="left"><bold>alc.</bold> <italic>units p. week</italic></td>
<td valign="top" align="center">3.2</td>
<td valign="top" align="center">(&#x00B1;4.8)</td>
<td/>
<td/>
<td/>
<td/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<attrib><italic>Alc, Alcohol consumption; BL, baseline; BMI, body mass index; average HR, heart rate; HF, LF, High- and Low-frequency; and their ratio, LF/HF in log-normalized and absolute values, average VAS, visual analog scale rating of 2&#x00B0;C cold-pain stimulation, rSBP, resting systolic blood pressure; M, Mean values as well as SD, standard deviations are reported. p.d., per day; p.w., per week; Bpm, beats per minute.</italic></attrib>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="S3.SS2">
<title>The Effect of Cold Pain on HRV Measures</title>
<p>We found an overall increase in logLF-HRV from Baseline to Cold-pain [mean (SD) = 2.91 (0.41) and 2.94 (0.46), respectively; <italic>F</italic><sub>(</sub><sub>1</sub>,<sub>17</sub><sub>)</sub> = 7.79, <italic>p</italic> = 0.013]. Whether the HRV data was sampled during the first or second half (interval) of each experimental condition did not affect this result [<italic>F</italic><sub>(</sub><sub>1</sub>,<sub>17</sub><sub>)</sub> = 1.39, <italic>p</italic> = 0.25] and there was no significant difference between intervals across conditions (<italic>F</italic> &#x003C; 1). As regards logHF-HRV, we did not observe a significant difference between Baseline and Cold-pain conditions (<italic>F</italic> &#x003C; 1), nor was there an effect of Interval (<italic>F</italic> &#x003C; 1) or a Condition &#x00D7; Interval interaction effect (<italic>F</italic> &#x003C; 1).</p>
</sec>
<sec id="S3.SS3">
<title>Correlation Between HRV Measures and Pain Ratings</title>
<p>HRV as measured during any of the experimental conditions was not associated with subjective cold pain intensity ratings (<italic>p</italic> &#x003E; 0.05 for both logLF-HRV and logHF-HRV).</p>
</sec>
<sec id="S3.SS4">
<title>Seed-Based Resting-State fMRI Results</title>
<sec id="S3.SS4.SSS1">
<title>Identification of Baseline Resting State Networks</title>
<sec id="S3.SS4.SSS1.Px1">
<title>dACC-seed</title>
<p>The dACC ROI was functionally connected with clusters in the left insula, bilateral superior frontal, precentral and postcentral gyri, the precuneus and the posterior cingulate cortex (<xref ref-type="fig" rid="F1">Figure 1A</xref> and <xref ref-type="table" rid="T2">Table 2</xref>).</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption><p>Regions of interest used in the seed-based analysis and their associated RSNs at baseline and during Cold-pain. <bold>(A)</bold> dACC ROI, MNI<sub>xyz</sub> = (2, 8, 38). This ROI had a 7 mm spherical radius. Anatomically, the dACC-seed lies in Brodmann area 24. <bold>(B)</bold> PAG ROI, MNI<sub>xyz</sub> = (0, &#x2013;30, &#x2013;1), the size was 3 mm spherical radius, and the seed was positioned in the anatomical midline. ROIs (in magenta) are overlaid on MNI-standardized T1-weighted images for visualization purposes. Color bars indicate <italic>t</italic> scores.</p></caption>
<graphic xlink:href="fnins-14-00147-g001.tif"/>
</fig>
<table-wrap position="float" id="T2">
<label>TABLE 2</label>
<caption><p>Resting state networks associated with the PAG and dACC seed regions.</p></caption>
<table cellspacing="5" cellpadding="5" frame="hsides" rules="groups">
<thead>
<tr>
<td valign="top" align="left" colspan="2"></td>
<td valign="top" align="center" colspan="2"><bold>Cluster</bold><hr/></td>
<td valign="top" align="center" colspan="2"><bold>Voxel</bold><hr/></td>
</tr>
<tr>
<td valign="top" align="left"><bold>Contrast</bold></td>
<td valign="top" align="left"><bold>Brain area</bold></td>
<td valign="top" align="center"><bold> <italic>k</italic></bold></td>
<td valign="top" align="center"><bold><italic>p FWE</italic></bold></td>
<td valign="top" align="center"><bold><italic>T (F)</italic></bold></td>
<td valign="top" align="center"><bold><italic>MNI xyz</italic></bold></td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left" colspan="3"><italic>Positive association with PAG seed</italic></td>
<td/>
<td valign="top" align="center"/>
<td/>
</tr>
<tr>
<td/>
<td valign="top" align="left">Left hippocampus</td>
<td valign="top" align="center">217110</td>
<td valign="top" align="center">&#x003C;0.001</td>
<td valign="top" align="center">17.30</td>
<td valign="top" align="center">&#x2212;14 &#x2212;18 &#x2212;18</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Right hippocampus</td>
<td valign="top" align="center"></td>
<td/>
<td valign="top" align="center">14.08</td>
<td valign="top" align="center">22 &#x2212;14 &#x2212;22</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Posterior cingulate cortex</td>
<td valign="top" align="center"></td>
<td/>
<td valign="top" align="center">13.46</td>
<td valign="top" align="center">20 &#x2212;42 &#x2212;2</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Thalamus</td>
<td valign="top" align="center"></td>
<td/>
<td valign="top" align="center">9.37</td>
<td valign="top" align="center">12 &#x2212;36 &#x2212;2</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Cerebellum</td>
<td valign="top" align="center"></td>
<td/>
<td valign="top" align="center">9.33</td>
<td valign="top" align="center">2 &#x2212;52 &#x2212;12</td>
</tr>
<tr>
<td valign="top" align="left" colspan="2"><italic>Positive association with dACC seed</italic></td>
<td/>
<td/>
<td valign="top" align="center"/>
<td/>
</tr>
<tr>
<td/>
<td valign="top" align="left">Middle cingulate cortex</td>
<td valign="top" align="center">921610</td>
<td valign="top" align="center">&#x003C;0.001</td>
<td valign="top" align="center">32.52</td>
<td valign="top" align="center">0 &#x2212;6 36</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Left Insula</td>
<td valign="top" align="center"></td>
<td/>
<td valign="top" align="center">25.04</td>
<td valign="top" align="center">&#x2212;30 20 0</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Superior frontal gyrus</td>
<td valign="top" align="center"></td>
<td/>
<td valign="top" align="center">19.65</td>
<td valign="top" align="center">24 &#x2212;4 52</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Posterior cingulate cortex/Precuneus</td>
<td valign="top" align="center"></td>
<td/>
<td valign="top" align="center">18.75</td>
<td valign="top" align="center">18 &#x2212;40 42</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Parietal operculum</td>
<td valign="top" align="center"></td>
<td/>
<td valign="top" align="center">18.56</td>
<td valign="top" align="center">52 &#x2212;32 20</td>
</tr>
<tr>
<td valign="top" align="left" colspan="2"><italic>FC changes during cold pain- dACC seed</italic></td>
<td/>
<td/>
<td valign="top" align="center"/>
<td/>
</tr>
<tr>
<td/>
<td valign="top" align="left">Superior forntal gyrus</td>
<td valign="top" align="center">588</td>
<td valign="top" align="center">0.001</td>
<td valign="top" align="center">4.66</td>
<td valign="top" align="center">&#x2212;12 50 18</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Anterior cingulate cortex</td>
<td/>
<td/>
<td valign="top" align="center">4.32</td>
<td valign="top" align="center">&#x2212;8 38 4</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Frontal lobe</td>
<td/>
<td/>
<td valign="top" align="center">4.00</td>
<td valign="top" align="center">&#x2212;12 46 30</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<attrib><italic>PAG, periaqueductal gray; ACC, anterior cingulate cortex; FC, functional connectivity.</italic></attrib>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="S3.SS4.SSS1.Px2">
<title>PAG-seed</title>
<p>The resting network of our PAG-seed consisted of a cluster in bilateral hippocampus, posterior cingulate cortex, thalamus and the cerebellum as well as ventro-medial prefrontal cortex (<xref ref-type="fig" rid="F1">Figure 1B</xref> and <xref ref-type="table" rid="T2">Table 2</xref>).</p>
</sec>
</sec>
<sec id="S3.SS4.SSS2">
<title>The Effect of Cold-Pain on Resting-State Networks</title>
<p>Functional connectivity of the dACC increased in the Cold-pain condition with clusters in the contralateral rostral ACC, superior frontal gyrus, and the frontal pole (<xref ref-type="fig" rid="F2">Figure 2A</xref> and <xref ref-type="table" rid="T2">Table 2</xref>). Upon noxious cold stimulation, functional connectivity of the PAG with the precuneus increased [results reported in <xref ref-type="bibr" rid="B75">Makovac et al. (2019)</xref>].</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption><p>Resting state fMRI results: <bold>(A)</bold> changes of dACC resting state network (RSN) with cold-pain stimulation, <bold>(B)</bold> Changes in dACC RSN associated with logLF-HRV as measured during cold-pain, <bold>(C)</bold> Baseline PAG functional connectivity with the vmPFC was associated with both logLF-HRV during cold-pain and participants&#x2019; pain ratings (VAS). Color bars indicate t scores.</p></caption>
<graphic xlink:href="fnins-14-00147-g002.tif"/>
</fig>
</sec>
<sec id="S3.SS4.SSS3">
<title>The Association Between RSNs and Pain-Related HRV Alterations</title>
<p>During Cold-pain, a positive association was observed between logLF-HRV and the functional connectivity between dACC and vmPFC (<xref ref-type="fig" rid="F2">Figure 2B</xref>). We did not observe any significant association between PAG functional connectivity and pain-induced HRV alterations.</p>
</sec>
<sec id="S3.SS4.SSS4">
<title>The Inter-Relationship Between RSNs, Pain-Related HRV Alterations and Pain Ratings</title>
<p>We tested whether baseline functional connectivity with PAG predicts both pain-related HRV reactions and pain ratings. Baseline functional connectivity between PAG and vmPFC expressed a significant logLF-HRV &#x00D7; cold pain ratings interaction. This effect was driven principally by the co-expression of a positive correlation with logLF-HRV during cold pain and a negative correlation with pain ratings. Thus, stronger baseline PAG-vmPFC connectivity was associated on one hand with stronger autonomic reaction during cold pain and on the other hand with lower subjective perception of cold pain (<xref ref-type="fig" rid="F2">Figure 2C</xref>).</p>
</sec>
</sec>
<sec id="S3.SS5">
<title>Baseline HRV as Predictor of Functional Connectivity Changes to Cold Pain</title>
<p>Higher baseline logLF-HRV measures predicted decreases in functional connectivity between dACC and superior frontal gyrus/dorsolateral prefrontal areas and between dACC and left AI (<xref ref-type="fig" rid="F3">Figure 3A</xref> and <xref ref-type="table" rid="T3">Table 3</xref>) during cold pain. Baseline logLF-HRV values predicted an increase in functional connectivity between the PAG<sup><xref ref-type="fn" rid="footnote2">2</xref></sup> and precuneus, and a decrease between the PAG and the right dorso-lateral prefrontal cortex (<xref ref-type="fig" rid="F3">Figure 3B</xref> and <xref ref-type="table" rid="T3">Table 3</xref>).</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption><p>Low frequency heart rate variability (LF-HRV) at baseline predicts functional connectivity changes (&#x0394; FC) of both seed regions upon cold-pain stimulation. <bold>(A)</bold> Baseline logLF-HRV predicted a decrease in dACC functional connectivity with regions in the right dorsolateral prefrontal area, frontal pole, and the right central opercular cortex. <bold>(B)</bold> Baseline logLF-HRV predicted a decrease during cold pain in functional connectivity between the PAG and right dorsolateral prefrontal cortex and an increase between the PAG and the precuneus. Color bars indicate t scores.</p></caption>
<graphic xlink:href="fnins-14-00147-g003.tif"/>
</fig>
<table-wrap position="float" id="T3">
<label>TABLE 3</label>
<caption><p>Brain areas showing an association with HRV and cold pain.</p></caption>
<table cellspacing="5" cellpadding="5" frame="hsides" rules="groups">
<thead>
<tr>
<td valign="top" align="left"></td>
<td/>
<td valign="top" align="center" colspan="4"><bold>Cluster</bold><hr/></td>
<td valign="top" align="center"><bold>Voxel</bold><hr/></td>
</tr>
<tr>
<td/>
<td valign="top" align="left"><bold>Brain area</bold></td>
<td valign="top" align="center"><bold><italic>k</italic></bold></td>
<td valign="top" align="center"><bold><italic>p FWE</italic></bold></td>
<td valign="top" align="center"><bold><italic>Side</italic></bold></td>
<td valign="top" align="center"><bold><italic>T (F)</italic></bold></td>
<td valign="top" align="center"><bold><italic>MNI</italic><sub>xyz</sub></bold></td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><bold>(1)</bold></td>
<td valign="top" align="left" colspan="6"><bold>Association between dACC functional connectivity and LF-HRV during cold-pain</bold></td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Medial prefrontal cortex</td>
<td valign="top" align="center">2296</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="center">L</td>
<td valign="top" align="center">6.04</td>
<td valign="top" align="center">&#x2212;18 50 14</td>
</tr>
<tr>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">6 64 12</td>
</tr>
<tr>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">2 48 20</td>
</tr>
<tr>
<td valign="top" align="left"><bold>(2)</bold></td>
<td valign="top" align="left" colspan="6"><bold>Association between baseline PAG functional connectivity and both LF-HRV during cold-pain and cold pain ratings</bold></td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Medial prefrontal cortex</td>
<td valign="top" align="center">2659</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="center">B</td>
<td valign="top" align="center">7.87</td>
<td valign="top" align="center">8 62 &#x2212;2</td>
</tr>
<tr>
<td valign="top" align="left"><bold>(3)</bold></td>
<td valign="top" align="left" colspan="6"><bold>Baseline LF-HRV as a predictor of functional connectivity changes during cold-pain</bold></td>
</tr>
<tr>
<td valign="top" align="left"><bold>3.a)</bold></td>
<td valign="top" align="left"><bold>dACC seed</bold></td>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td/>
<td valign="top" align="left">Superior frontal gyrus</td>
<td valign="top" align="center">825</td>
<td valign="top" align="center">0.001</td>
<td valign="top" align="center">R</td>
<td valign="top" align="center">6.8</td>
<td valign="top" align="center">10 56 22</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Frontal pole</td>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">24 56 22</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Dorsolateral prefrontal cortex</td>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">6 66 28</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Anterior insula</td>
<td valign="top" align="center">539</td>
<td/>
<td valign="top" align="center">L</td>
<td/>
<td valign="top" align="center">&#x2212;36 16 &#x2212;4</td>
</tr>
<tr>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">&#x2212;50 12 &#x2212;10</td>
</tr>
<tr>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">&#x2212;40 2 &#x2212;2</td>
</tr>
<tr>
<td valign="top" align="left"><bold>b)</bold></td>
<td valign="top" align="left"><bold>PAG seed</bold></td>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td/>
<td valign="top" align="left">Precuneus</td>
<td valign="top" align="center">941</td>
<td valign="top" align="center">0.194</td>
<td valign="top" align="center">B</td>
<td valign="top" align="center">6.55</td>
<td valign="top" align="center">4 &#x2212;58 2</td>
</tr>
<tr>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">&#x2212;10 &#x2212;56 6</td>
</tr>
<tr>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">&#x2212;22 &#x2212;60 28</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Dorsolateral prefrontal cortex</td>
<td valign="top" align="center">1353</td>
<td valign="top" align="center">0.251</td>
<td valign="top" align="center">L</td>
<td valign="top" align="center">6.33</td>
<td valign="top" align="center">&#x2212;46 26 36</td>
</tr>
<tr>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">&#x2212;44 34 28</td>
</tr>
<tr>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">&#x2212;20 58 &#x2212;10</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
<sec id="S4">
<title>Discussion</title>
<p>The aim of this study was to examine the relationship between HRV and brain functional connectivity during painful experimental stimulation. Specifically, we investigated the effect of a tonic noxious cold stimulus in a group of healthy participants, implementing simultaneous rs-fMRI and HRV sampling. Our results support a role of ANS activity, as indexed by HRV, in brain connectivity during pain and specify functional connections of our dACC and PAG seed regions that are associated with measurements of LF-HRV. In particular, we identified a three-way relationship between HRV, cortical brain networks known to underpin pain processing, and participants&#x2019; subjectively reported pain experiences. Baseline PAG-vmPFC functional connectivity was associated with higher LF-HRV during cold stimulation and lower subjective cold pain ratings, suggesting that the role of the ANS in the modulation of nociception might relate (at least in part) to PAG-cortical functional connections.</p>
<p>Our findings combine two separate streams of research: the association between LF-HRV and pain and the role of PAG functional connectivity in pain perception. Higher baroreflex activation and parasympathetic activity (indexed by LF and HF-HRV) prior to and during noxious stimulation is associated with reduced pain intensity or higher pain thresholds (<xref ref-type="bibr" rid="B37">Duschek et al., 2007</xref>; <xref ref-type="bibr" rid="B7">Appelhans and Luecken, 2008</xref>; <xref ref-type="bibr" rid="B86">Nahman-Averbuch et al., 2016</xref>; <xref ref-type="bibr" rid="B133">Tracy et al., 2018</xref>). In addition to the well-established role of the PAG in descending nociceptive modulation (<xref ref-type="bibr" rid="B91">Ossipov et al., 2010</xref>; <xref ref-type="bibr" rid="B17">Benarroch, 2012</xref>), PAG-mPFC connectivity (<xref ref-type="bibr" rid="B114">Sprenger et al., 2011</xref>) and activity of the mPFC alone (<xref ref-type="bibr" rid="B21">Bogdanov et al., 2015</xref>) have been linked to the efficacy of endogenous analgesic mechanisms and to vagal cardiac control (<xref ref-type="bibr" rid="B33">Critchley et al., 2011</xref>). A recent brainstem-focused fMRI study showed that subjective pain intensity is not only influenced by the reaction of the brain to a noxious stimulus, but also by the connectivity of the PAG prior to stimulation (<xref ref-type="bibr" rid="B121">Stroman et al., 2018</xref>). These results indicate that the PAG is functionally associated with the hypothalamus and several brainstem areas involved in autonomic regulation, highlighting the potential importance of homeostatic autonomic control in the descending modulation of nociception. Our findings provide direct support for this theory. Meta-analytical evidence shows that right mPFC is associated with HRV during both emotional and cognitive/motor tasks (<xref ref-type="bibr" rid="B128">Thayer et al., 2012</xref>). Others have reported an association between mPFC resting state connectivity and HRV, often linked to emotion regulation. Our findings expand this work by showing that the functional communication between the PAG and the mPFC is associated with LF-HRV reactions to noxious stimulation as well as the subjective perception of cold pain intensity.</p>
<p>During noxious cold stimulation, functional connectivity of the right dACC with the mPFC was positively correlated with LF-HRV. A reduction in the dACC-(ventral)mPFC functional connectivity has been shown to partially mediate heart rate increases during socially stressing experiences (<xref ref-type="bibr" rid="B138">Wager et al., 2009</xref>). Whilst we did not formally assess mediation effects, our finding extends this notion to include pain, an experience which requires both emotional and physiological regulation. In more general terms, this finding underlines the known role of the mPFC in context appraisal and autonomic adaptation, not least in situations of threat (<xref ref-type="bibr" rid="B106">Schiller et al., 2008</xref>; <xref ref-type="bibr" rid="B128">Thayer et al., 2012</xref>).</p>
<p>Higher baseline LF-HRV, which is often considered as a measure of baroreflex activation (<xref ref-type="bibr" rid="B45">Goldstein et al., 2011</xref>), was associated with an increase in PAG-precuneus functional connectivity during tonic noxious stimulation. Areas of the DMN (including the precuneus) show reduced functional connectivity during pain (<xref ref-type="bibr" rid="B10">Baliki et al., 2008</xref>; <xref ref-type="bibr" rid="B62">Kong et al., 2010a</xref>). In view of the opinion of higher LF-HRV as a likely anti-nociceptive mechanism, sustained connectivity between the PAG and the precuneus is thus consistent with a reduced nociception-associated brain response. A potential underlying mechanism is offered by <xref ref-type="bibr" rid="B65">Kucyi et al. (2013)</xref>, who associated greater functional connectivity between PAG and precuneus/mPFC with participants&#x2019; tendencies to disengage their attention from the noxious stimulation (&#x201C;mind wandering&#x201D;), thus achieving pain relief by means of distraction (<xref ref-type="bibr" rid="B115">Sprenger et al., 2012</xref>). Possibly, effective engagement of descending pain modulation as associated with LF-HRV allows for, or is part of, participants&#x2019; natural tendencies to &#x201C;take their minds off the pain.&#x201D; Accordingly, this finding offers preliminary support for theories of a protective role of mind wandering in optimizing states of bodily arousal during challenges to homeostasis (<xref ref-type="bibr" rid="B9">Baars, 2010</xref>; <xref ref-type="bibr" rid="B92">Ottaviani et al., 2013</xref>).</p>
<p>Higher baseline LF-HRV was also associated with a stronger decrease in functional connectivity between both the PAG and dACC seeds with the dlPFC. DlPFC activity is commonly reported in pain experiments. Functionally part of the central executive-control network (CEN), the dlPFC shows increased activation during the performance of cognitively demanding tasks (<xref ref-type="bibr" rid="B108">Seeley et al., 2007</xref>) and is associated with cognitive difficulties seen in chronic pain (<xref ref-type="bibr" rid="B27">Bushnell et al., 2013</xref>). Albeit based on experimentally induced pain, our results suggest a link between cognitive and homeostatic systems, and indicate a possible common neuronal substrate for cognitive difficulties (<xref ref-type="bibr" rid="B6">Apkarian et al., 2004</xref>; <xref ref-type="bibr" rid="B27">Bushnell et al., 2013</xref>) and HRV alterations (<xref ref-type="bibr" rid="B79">Meeus et al., 2013</xref>) often reported in chronic patients. Functional connectivity of the dACC with the dlPFC has previously been shown to co-vary with LF-HRV (<xref ref-type="bibr" rid="B30">Chang et al., 2013</xref>), supporting a role of the ANS in this functional link.</p>
<p>Higher baseline resting LF-HRV also related to decreases in functional connectivity between the dACC and the left AI during noxious stimulation. Activity of the insular cortex is frequently coupled to that of the cingulate cortex. Together, they form part of an interoceptive network which facilitates emotion and self-awareness but also modulates autonomic function (<xref ref-type="bibr" rid="B32">Craig, 2002</xref>). A specific role in cardiovascular control has been postulated (<xref ref-type="bibr" rid="B85">Nagai et al., 2010</xref>). Importantly, insular activity is associated with top down suppression of baroreflex activity induced by a stress challenge (<xref ref-type="bibr" rid="B43">Gianaros et al., 2012</xref>). We suggest that the ANS-associated connectivity between AI and dACC plays a central role in interoception during pain and might also contribute to baroreflex-induced anti-nociception (as reviewed by <xref ref-type="bibr" rid="B122">Suarez-Roca et al., 2018</xref>). Also, activity in the AI is commonly seen during pain (<xref ref-type="bibr" rid="B71">Legrain et al., 2011</xref>). More specifically, the AI is involved in the cognitive evaluation of pain (<xref ref-type="bibr" rid="B24">Brooks et al., 2002</xref>; <xref ref-type="bibr" rid="B139">Wiech et al., 2010</xref>; <xref ref-type="bibr" rid="B109">Segerdahl et al., 2015</xref>). Furthermore, combined AI and ACC activation was found to relate to the emotional components of pain, such as psychological pain, and empathy for a loved one in pain (<xref ref-type="bibr" rid="B110">Singer et al., 2004</xref>; <xref ref-type="bibr" rid="B54">Immordino-Yang et al., 2009</xref>). Less functional connectivity between the dACC and AI with high baseline LF-HRV is thus consistent with maintained baroreflex activity during noxious stimulation, and reduced activity in an interoceptive and cognitive/emotional key area of pain perception.</p>
<p>Our data provide reason for further investigation into the role of autonomic cardiovascular modulation in central network dynamics. The mPFC and the precuneus are considered part of the DMN (<xref ref-type="bibr" rid="B41">Fox and Raichle, 2007</xref>), whilst the dlPFC is a core node in the CEN (<xref ref-type="bibr" rid="B108">Seeley et al., 2007</xref>). The dACC and the AI, on the other hand, form the salience network (SN) which responds to the subjective salience of cognitive, emotional, and homeostatic stimuli. The SN is thought to play a critical role in switching between the off-task DMN and the on-task CEN (<xref ref-type="bibr" rid="B116">Sridharan et al., 2008</xref>; <xref ref-type="bibr" rid="B80">Menon and Uddin, 2010</xref>). Both DMN and CEN alterations have been described in chronic pain states (<xref ref-type="bibr" rid="B10">Baliki et al., 2008</xref>; <xref ref-type="bibr" rid="B29">Cauda et al., 2009</xref>; <xref ref-type="bibr" rid="B76">Malinen et al., 2010</xref>; <xref ref-type="bibr" rid="B88">Napadow et al., 2010</xref>, <xref ref-type="bibr" rid="B87">2012</xref>; <xref ref-type="bibr" rid="B123">Tagliazucchi et al., 2010</xref>; <xref ref-type="bibr" rid="B22">Bolwerk et al., 2013</xref>; <xref ref-type="bibr" rid="B58">Jiang et al., 2016</xref>; <xref ref-type="bibr" rid="B1">Alshelh et al., 2018</xref>; <xref ref-type="bibr" rid="B3">Androulakis et al., 2018</xref>). Here we demonstrated that by phenotyping participants based on LF-HRV, a physiological variable, we were able to predict changes in components of these functional networks during the experience of a noxious stimulus. Albeit not having assessed network dynamics explicitly, these findings suggest that the ANS may be implicated in the salience network-mediated switch from default to central cognitive-executive modes during the experience of tonic experimental pain. Speculatively, a pronounced LF-HRV at baseline (interpreted as a more pronounced engagement of the baroreflex) might predict a reduced tendency for a SN-mediated switch of network dynamics toward an executive mode, whilst LF-HRV during the experience of pain might be associated with maintained DMN dynamics. It remains to be determined whether these initial neurophysiological findings apply in patients with chronic pain.</p>
<p>Finally, hierarchical interactions between cognitive, emotional and autonomic processes are also a quintessential component of the neurovisceral integration (NVI) model, as proposed by <xref ref-type="bibr" rid="B130">Thayer and Lane (2000)</xref>. This model postulates that cardiac vagal tone (i.e., the contribution of the parasympathetic nervous system to cardiac regulation) is an indicator of the functional balance between neural networks involved in the regulation of emotions and cognition (<xref ref-type="bibr" rid="B130">Thayer and Lane, 2000</xref>). This Central Autonomic Network (CAN) (<xref ref-type="bibr" rid="B14">Benarroch, 1993</xref>) consists of parts of the prefrontal cortex (anterior cingulate, insula, orbitofrontal, and ventromedial cortex), limbic cortex (amygdala and hypothalamus), and brain stem areas (i.e., PAG and ventromedial medulla). Our data fits with this model, as they suggest that the resting cardiac autonomic tone (possibly related to the baroreflex) and the cardiac autonomic reactivity to pain is associated with the functional organization of some pain-related networks (i.e., those related to the dACC and PAG, involving other structures of the CAN such as vmPFC and anterior insula), which in turn is related to the amount of experienced pain. It is of note, however, that our data do not strictly explore the vagal influence on cardiac regulation, but rather the reactivity of the baroreflex, which is more representative of sympatho/vagal balance. Importantly, the NVI model suggests that HRV is a measure of the flexibility of the entire brain-body system, with the view that flexible systems are adaptive and responsive to the environment, allowing for functional oscillations between different states (<xref ref-type="bibr" rid="B112">Smith et al., 2017</xref>). In the context of pain, we argue that an individual with an adequate ANS reaction to pain is more efficient in triggering those mechanisms which have the aim of re-establishing adaptive homeostasis (i.e., descending pain modulatory mechanisms). Future studies should aim to explore this model in clinical populations.</p>
<p>We acknowledge some methodological limitations: First, the method of HRV acquisition, photoplethysmography (PPG), is not considered gold standard. Despite this, individual studies such as <xref ref-type="bibr" rid="B44">Gil et al. (2010)</xref> show that while subjects are at rest, PPG is a good surrogate measure for ECG-derived HRV. Sufficient accuracy under stationary conditions has also been confirmed by a review on the topic (<xref ref-type="bibr" rid="B105">Sch&#x00E4;fer and Vagedes, 2013</xref>). Furthermore, the inter-session reliability of our measurements was good (see <xref ref-type="supplementary-material" rid="DS1">Supplementary Material S3</xref>), providing confidence in our method of capturing HRV. We also acknowledge a relatively small sample size; this study was formally powered to detect the effect of a noxious stimulation on functional brain networks rather than to determine interactions between functional networks and the ANS. Future studies should aim to replicate our findings with larger samples in order to validate the robustness and reliability of our results. Lastly, and this note of caution applies to many studies linking brain activity to function, the associations between HRV, functional connectivity and subjective pain does by no means imply causality. Further studies will have to elucidate if there is a causal relationship between the above findings. Dynamic causal modeling techniques applied to brain connectivity data (<xref ref-type="bibr" rid="B42">Friston, 2009</xref>) offer a potential means to conduct these investigations.</p>
<p>In conclusion, we have demonstrated that the engagement of brain regions involved in the cognitive, emotional and limbic processing of pain is intimately linked to autonomic profiles and subjective pain sensitivity. As the first study to explore the association between pain-related HRV and brain functional connectivity, we provide an improved understanding of the relationship between pain perception and autonomic cardiovascular control, likely involving the baroreflex.</p>
<p>Future research should determine whether this functional connectivity is altered in chronic pain, and if modulation of ANS activity might protect from chronic pain. Such data may catalyze the development and utility of ANS-targeted pain treatments, such as HRV biofeedback, vagal stimulation or baroreceptor activation therapies.</p>
</sec>
<sec id="S5">
<title>Data Availability Statement</title>
<p>At the date of publication, the datasets for this manuscript are not publicly available because of limited ethical approval to share participant data. Requests to access the datasets should be directed to EM (<email>elena.makovac@kcl.ac.uk</email>).</p>
</sec>
<sec id="S5a">
<title>Ethics Statement</title>
<p>The studies involving human participants were reviewed and approved by the Ethics Committee King&#x2019;s College London. The patients/participants provided their written informed consent to participate in this study.</p>
</sec>
<sec id="S6">
<title>Author Contributions</title>
<p>DH-S analyzed the data and wrote the manuscript. GC, OD, JJ, SM, OO&#x2019;D, JO&#x2019;M, AdLR, SW, and SM contributed substantially to the conception of the experiment and to the analysis of the data and reviewed the manuscript. EM conceived and conducted the experiment and contributed substantially to the writing and revision of the manuscript. MH supervised the team, contributed to the planning and conduction of the experiment, and made major contributions to the revision of the manuscript.</p>
</sec>
<sec id="conf1">
<title>Conflict of Interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
</body>
<back>
<fn-group>
<fn fn-type="financial-disclosure">
<p><bold>Funding.</bold> This work was funded by the Medical Research Council Experimental Medicine Challenge Grant (MR/N026969/1). MH, SM, and SW were also supported by the NIHR Biomedical Research Centre for Mental Health at the South London and Maudsley NHS Foundation Trust. JO&#x2019;M was supported by a Sir Henry Dale Fellowship jointly funded by the Welcome Trust and the Royal Society (Grant Number 206675/Z/17/Z) and a Medical Research Council (MRC) Centre grant (MR/N026063/1).</p>
</fn>
</fn-group>
<sec id="S8" sec-type="supplementary material"><title>Supplementary Material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fnins.2020.00147/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fnins.2020.00147/full#supplementary-material</ext-link></p>
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<fn-group>
<fn id="footnote1">
<label>1</label>
<p><ext-link ext-link-type="uri" xlink:href="http://marsbar.sourceforge.net/">http://marsbar.sourceforge.net/</ext-link></p></fn>
<fn id="footnote2">
<label>2</label>
<p>We identified one participant with PAG functional connectivity values outside inter-quartile difference criteria for outlier identification (<xref ref-type="bibr" rid="B134">Tukey, 1977</xref>). Accordingly, the participant was excluded from baseline logLF-HRV functional connectivity analyses and the final analysis was performed with 20 participants.</p></fn>
</fn-group>
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</article>