In this study, distinct functional connectivity networks for pain were revealed by fMRI-CPCA. The networks encompass a variety of brain regions consistently active in response to pain, including MI, SMA, cerebellum and SI (Component 1), the ACC, insular cortex, and thalamus (Component 2), and mPFC, hippocampus, para-hippocampus and precuneus (Component 4) (Apkarian et al.,
2005; Atlas et al.,
2014; Schweinhardt & Bushnell,
2010). Within participants, changes in perceived intensity related to low and high temperatures were associated with the magnitude of change in BOLD across networks. While falling short of accurate classification, the magnitude of BOLD activation in functional networks was significantly associated with pain intensity between participants. Future development of fMRI-CPCA in the context of pain is warranted to further explore the brain in pain.
Over and above capturing the activation of known pain regions, fMRI-CPCA integrated these brain regions into multiple functional networks. Although the specific parcellation observed here is unique, it is largely congruent with current perspectives on pain-related networks. In particular, evidence from PET and fMRI suggests that pain-activated regions are segregated into at least four distinct sub-networks: a sensory network for stimulus localization and intensity coding (Davis & Moayedi,
2013; Hofbauer et al.,
2001; Peyron et al.,
1999), an affective network for generating the aversive, unpleasant quality of a stimulus (Davis & Moayedi,
2013; Peyron et al.,
2000; Wilcox et al.,
2015), a cognitive network for attending to, anticipating and remembering the stimulus (Davis & Moayedi,
2013; Peyron et al.,
1999; Wilcox et al.,
2015), and a network of motor regions for pain avoidance (Davis & Moayedi,
2013; Wilcox et al.,
2015).
Component 1 (Sensorimotor Response)
Component 1, with prominent activation peaks in MI, SMA, and cerebellum, is most aptly described as a sensory and motor network. In the context of thermal stimulation, sensation and motor output may be related to an instinctive flexing or bracing, or a desire to move, in response to intense stimuli (Davis & Moayedi,
2013; Davis et al.,
2002). Hemodynamic response shapes (HDRs) for Component 1 showed that activation was in fact exclusive to higher intensity stimuli (temperatures above 44.3 °C). Component 1 also included prominent activations in SI, which, as a key cortical aspect of the lateral nociceptive system, is one of the first recipients of ascending pain signals through the spinothalamic tract (Davis & Moayedi,
2013; Fomberstein et al.,
2013; Vierck et al.,
2013; Yam et al.,
2018). SI’s inclusion in Component 1 suggests that, during thermal stimulation, motor processes are prioritized and closely coordinated with sensory-discriminative functions (e.g. determination of stimulus location and intensity). In theory, such close communication would be necessary for an effective pain avoidance response when stimulus intensity reaches noxious levels (Postorino et al.,
2017). This plausible role of Component 1 in generating pain-induced motor commands remains to be further explored; follow-up studies would benefit from monitoring physical movements in conjunction with other variables, allowing for the precise relationships between Component 1 activation intensity, stimulus intensity (e.g. temperature), motion, and pain perception to be determined.
A novel observation from fMRI-CPCA is the temporal overlap between visual areas and sensory-motor coupling, evidenced in Component 1. Their detection is likely an idiosyncratic capture of fMRI-CPCA, which avoids using regions-of-interest to spatially constrain the analysis. In fact, the anatomy of Component 1 replicates previous applications of fMRI-CPCA in other domains—specifically, it resembles a network consistently associated with sensorimotor response processes, featuring activations in lateralized MI, SMA, SI, cerebellum, and visual areas including the lateral occipital cortex (LO; Goghari et al.,
2017; Larivière et al.,
2017; Metzak et al.,
2011). In this case, sensorimotor-visual coupling was likely observed because of screen-related cues that coincided with stimulus presentation.
Component 2 (Attentional Pain Network)
In agreement with previous studies, Component 2 incorporated a large number of regions involved in pain, and combined sensory, affective and cognitive sub-networks (Davis & Moayedi,
2013; Wilcox et al.,
2015). For example, the most prominent activation peaks were found in SII and posterior insula (pIns; sensory-discriminative regions), dACC, aIns, and thalamus (affective-motivational regions), and dlPFC and IPL (cognitive-evaluative regions; Peyron et al.,
1999; Wilcox et al.,
2015). Based on this, Component 2 could reflect a unification of sensory, affective and cognitive processes (Melzack & Casey,
1968) into a coordinated pain response.
The blending of sub-networks is likely facilitated by their inter-connectivity at rest, provided by common nodes in ACC and aIns that serve as relay sites between sub-networks (Wilcox et al.,
2015). Importantly, the ACC and aIns are engaged in non-specific “salience detection”, where stimuli are selected based on their behavioural relevance, and attentional systems are primed to enable an effective response (Legrain et al.,
2011; Menon,
2015). Such a “salience network” receives axonal projections from sensory areas like the pIns, which are thought to provide the aIns with incoming sensory information (Menon & Uddin,
2010). The pattern of activation observed in Component 2 captures both attentional systems (i.e. the cognitive sub-network of pain) and sensory-discriminative elements like SII and pIns. Thus, Component 2 may represent a salience network-mediated response to salience—in this case thermal stimulation—or more precisely a sequential activation of sub-networks, i.e. sensory systems activate the salience network, which then activates cognitive systems for sustained attention. The directionality of sub-network relations is a matter of speculation, but it presents an interesting question for future investigation. Additionally, the putative attentional function of Component 2 may be further explored by analyzing its pain-induced response during experimental manipulations of attentional demand or stimulus salience; a larger effect of attention on network response than stimulus temperature would suggest an attentional role.
Component 4 (Default-Mode Network)
The tendency for brain areas to become deactivated during a task and engaged at rest gave rise to the original concept of the “default mode of brain function” (Shulman et al.,
1997; Raichle et al.,
2001). Since being originally characterized, research has emerged documenting the overall functional contributions of the default-mode network (DMN) to human behavior, including its relevance to mind-wandering, self-referential thought, mentalizing and semantic processing (Andrews-Hanna,
2012; Andrews-Hanna et al.,
2014; Christoff et al.,
2009).
Component 4 was comprised of deactivations in regions conventionally associated with the DMN, including the PCC, the AnG, and the amPFC. Such a deactivation departs from the proposed sub-network scheme discussed above (i.e. sensory, affective, cognitive, and motor sub-networks of pain; Davis & Moayedi,
2013; Wilcox et al.,
2015). However, the DMN has also been implicated in pain and so its detection here is not entirely unexpected. In chronic pain disorders, for example, the DMN shows a number of anatomical-functional alterations, including fragmentation between frontal and posterior regions (Baliki et al.,
2014), and strengthening of functional connections to aIns (Baliki et al.,
2014; Loggia et al.,
2013). In healthy individuals, heat-induced
deactivations in several DMN regions have been reported (Kong et al.,
2010), while some regions, like the hippocampus and precuneus, also predict pain ratings (in addition to stimulus intensity) by the magnitude of their deactivation (Atlas et al.,
2014).
As others have argued, pain-induced deactivations in the DMN may be part of an attentional response to pain (Kucyi et al.,
2013; Kucyi & Davis,
2015), where the DMN suppresses as attentional networks (e.g. Component 2) engage. This type of antagonistic relationship between the DMN and attentional networks has been documented extensively outside of pain imaging, along with the DMN’s “task-negative” tendencies (Anticevic et al.,
2012; Peng et al.,
2018). Future research would benefit from an analysis of DMN response to pain in the context of attentional manipulations. Alternatively, attention levels during a stimulus could be monitored to allow for an analysis of the relationships between DMN deactivation, DMN-Component 2 antagonism, pain perception and attention.
Also of note, several DMN regions, including the mPFC, hippocampus, and precuneus, have been associated with the regulation of pain (Goffaux et al.,
2014; Schweinhardt & Bushnell,
2010). Their involvement implies a potential role of the DMN, which might accomplish regulation by interacting with the periaqueductal gray (PAG)—part of a descending pathway for pain control—through the mPFC (Kucyi et al.,
2013). Thus, chronic pain disorders may be related, in part, to deficits in pain regulation caused by alterations to the DMN. This possibility requires further investigation and presents an important research objective due to its implications for chronic pain treatment.
Estimating Pain Within and Between Participants
Among intended applications of neuroimaging in the field of pain is the development of models to accurately classify an individual in pain. Previous attempts of this nature have adopted multivariate pattern analysis (MVPA; Haynes,
2015; van der Miesen et al.,
2019). In brief, MVPA uses machine learning algorithms to model behavioural responses (either ordinal or continuous variables) as a function of multiple voxels (or “features”) considered simultaneously (Moayedi et al.,
2018; van der Miesen et al.,
2019); predictions or classifications of mental states are then generated on independent “testing” data based on model parameters learned in the “training” set (Rosa & Seymour,
2014). In one notable study applying MVPA, a network of regression weights distributed over pain regions (the “neurologic pain signature” or NPS) tracked physical pain intensity
between individuals (Wager et al.,
2013; Woo et al.,
2015). Perhaps even more remarkable is that physical pain could be accurately distinguished from other types of pain (e.g., social; Wager et al.,
2013).
In this study, regression models provided some insight into the capacity of networks detected through fMRI-CPCA to be used for pain prediction, as components 1, 2 and 4 were significantly associated with pain ratings both within and between participants. Importantly, this was not a predictive model (networks were used to model in-sample ratings with no predictions generated on new or held-out data), and the findings should not be interpreted as direct evidence of prediction ability. However, networks did show potential to be used in predictive analyses given that in-sample estimation was moderately accurate, and, importantly, results were achieved without any a priori selection of brain regions, reflecting a distinct advantage of fMRI-CPCA compared to other approaches.
Of all networks, Component 2 was most strongly related to pain perception; the relationship was positive and consistently accounted for the largest proportion of within- and between-subject variability in pain. The value of Component 2 for predicting pain is intuitive, insofar as brain regions included in this network represent sensory, affective, and cognitive dimensions of pain (Melzack & Casey,
1968). The DMN was also important for pain estimation, with the magnitude of its deactivations being significantly related to perceived pain intensity, both within and between participants. The relationships of both networks to pain are corroborated by previous work that has identified several Component 2 regions—including SII, aIns, dACC, left cerebellum, and IPL—and DMN regions—including hippocampus and precuneus—as explicit mediators of pain (i.e. they mediate the relationship between stimulus intensity and pain rating; Atlas et al.,
2014).
The intensity of activation in Component 1 was unrelated to the intensity of perceived pain, mirroring the behaviour of SI itself, which codes pain information primarily in terms of sensory-discriminative attributes (Moulton et al.,
2012). This aspect of Component 1 (i.e. its independence from pain perception) is corroborated by mediation analyses that demonstrate a preference of sensory cortex and cerebellum to stimulus intensity over pain report (Atlas et al.,
2014), and implies that motor systems are mobilized in accordance with stimulus properties only; the perception of pain occurs elsewhere, and the intensity of motor commands is, on its own, an unreliable proxy for the intensity of that perception.
Despite significant associations, when converted into a classifier the model discriminated between pain and warmth with an accuracy of only 68.83%. While significantly greater than chance, sensitivity and specificity were low (estimated at 59.17% and 79.10%, respectively). Still, comparisons between components 1, 2 and 4 and the existing “neurological pain signature” (NPS) reveal a high degree of overlap. Common regions include aIns, pIns, supramarginal gyrus, thalamus, and IPL. Further, the NPS included negative predictive weights in regions that were deactivated in Component 4, including PCC, precuneus and mPFC (Wager et al.,
2013). These anatomical similarities raise the possibility that accurate predictions of pain could be generated from components 1, 2 and 4 if specific regional activations (compared to an overall estimate of activation in the entire network) were accounted for using MVPA (Allefeld & Haynes,
2015). By avoiding spatial averaging, MVPA accounts for signal non-uniformities between voxels, and exploits these differences in response signal as a source of predictive information (Hebart & Baker,
2018).
Crucially, the predictive potential shown by components indicates that fMRI-CPCA may provide a useful tool for determining appropriate anatomical targets for MVPA. This is important because a critical step in the MVPA framework is the selection of “features” with which to train the machine learning algorithm (Rosa & Seymour,
2014). Features are typically a subset of voxels, whose activations will be related to the behavioural response by the algorithm (Allefeld & Haynes,
2015), and are selected from a region- or regions-of-interest (based on prior knowledge) or from the entire brain using dimensionality reduction techniques like PCA (van der Miesen et al.,
2019). Restricting the analysis to relevant regions is important to mitigate the problem of features exceeding the number of observations, which may lead to overfitted models and interpretive challenges (van der Miesen et al.,
2019). In the case of the NPS, features were selected a priori from a collection of well-established pain regions (Wager et al.,
2013). By contrast, fMRI-CPCA would allow features to be selected from the predominant functional networks involved in pain perception, without relying on prior assumptions about relevant spatial or temporal response patterns. fMRI-CPCA thus provides an opportunity to select connectivity-based features (Rosa & Seymour,
2014) that are unbiased, data-driven and task-related.
As a final point, results from multiple regressions are not only relevant to pain prediction, but also reflect on network functions proposed earlier, specifically the roles of Component 2 and the DMN as attention networks. In the regressions, Component 2 and the DMN displayed opposite relationships to pain; higher pain was associated with greater activation in Component 2 but greater suppression in the DMN, both within and between participants. This is an extension of the pattern shown by estimated HDRs, where Component 2 became active during stimulation while the DMN became suppressed. Together, these findings suggest that Component 2 and the DMN assume an antagonistic configuration during pain, and that greater antagonism (i.e. greater separation in terms of activation) equates to a heightened perception of pain.
Based on neuroimaging literature, this antagonism is likely indicative of an ongoing attentional response. Component 2 included known salience network hubs in ACC and aIns, as well as cognitive pain regions associated with attention to pain, and the DMN’s role in attention has been well-documented. For example, the DMN tends to form anticorrelated relationships with frontoparietal attention networks during cognitively demanding tasks (Dixon et al.,
2017; Menon,
2015; Sridharan et al.,
2008), with greater deactivation predicting improved task performance (Anticevic et al.,
2012). Furthermore, attention deficits are generally associated with increased DMN activation (Bonnelle et al.,
2011; Weissman et al.,
2006; Danckert & Merrifield,
2018). In the context of pain, DMN deactivation is especially pronounced when participants report
attending to pain, and less so when participants mind-wander away from pain (Kucyi et al.,
2013). Thus, the deactivation of DMN observed here likely signifies attention to pain. The simultaneous activation of Component 2—which included several regions known to be involved in attention—mirrors the stereo-typical antagonism between DMN and frontoparietal networks that underlies attention (Anticevic et al.,
2012). In sum, these networks appear to contribute to pain perception by working together, in an anticorrelated fashion, as part of an attentional response process; the greater the attention, the greater the antagonism between networks and the greater the pain intensity.
Technical Considerations
Based on the literature discussed in sections above, it is possible to infer the functionality of each network. However, these inferences are speculative and are not necessarily validated by any direct experimental evidence obtained here; instead they rely on prior notions about the functional contributions of regions or networks detected. Importantly, the fMRI-CPCA framework provides an opportunity to more robustly characterize network function during a task. This is done by comparing the HDRs estimated for each network across task conditions to determine how different combinations of independent variables impact network behaviour. Statistical comparisons can be made using repeated-measures ANOVAs, with within-subject factors given by time and independent variables of interest (e.g. temperature level in this study). By carefully manipulating experimental conditions, cognitive processes can be dissociated from each other, and by interpreting main and interaction effects of factors on HDRs, networks can be related to specific aspects of cognition operationalized by task conditions. Comparisons can also be made between populations of interest by adding between-subject factors that define group membership. In this way, network alterations or deficits associated with diagnostic categories—such as chronic pain disorders—can be investigated.
It should be noted that the HDRs estimated by fMRI-CPCA are well-suited to making inferences about cognitive function; this is because fMRI-CPCA uses Finite Impulse Response (FIR)-basis sets to encode brain activity associated with task-timing, which are essentially dummy regressors for stimulus presentation timing that make no assumptions about the shape of the expected response. For this reason, the technique detects responses (and by extension, functional networks) elicited by cognitive processes that may go unnoticed in more traditional analyses, where the expected response is produced by convolving stimulus functions with canonical hemodynamic response functions (Henson & Friston,
2007; Henson et al.,
2001; Lindquist,
2008; Lindquist et al.,
2009). Detailed analysis of HDR shapes evoked in components 1, 2 and 4, under different experimental conditions, is therefore warranted to achieve a robust determination of network function.
Limitations
Our study has a number of limitations to consider. First, we did not include a protocol for model validation when evaluating pain predictions and classifications made with multiple linear regression; the ability of the model to predict or classify pain in independent samples therefore remains unverified. Validation techniques—including cross-validation, hold-out validation, or bootstrapping—are common practice in decoding analyses to ensure that models generalize to out-of-sample data (Kohavi,
1995; van der Miesen et al.,
2019). We did not apply these here because of properties of the data (primarily its small sample size of 30), which made a conventional approach challenging (e.g. some subjects never reported a stimulus as painful). Future research is needed to formally validate the pain predictive value of these networks.
In theory, the regression model obtained here is generalizable to new and independent data. The basic procedure would involve application of the regression coefficients obtained to the network activations estimated in a new individual to generate a predicted pain rating. This would require first obtaining an individual’s activation data during a thermal pain task, analysing their brain activity using fMRI-CPCA, and “classifying” the networks elucidated through in-house programs recently developed to determine which of the new individual’s networks most closely match with the networks that inform the current model (Percival et al.,
2020). The HDR shapes associated with the correct networks would have to be averaged across an appropriate time interval (or an equivalent time interval to the current study) to generate estimates of network activation intensity, and the regression model obtained here would then be applied to network activation intensities to generate a predicted pain rating. The classification procedure referred to above has been utilized previously and involves correlating the loadings of networks obtained with the loadings of “template” images of networks, across a set of characteristic slices that define the individuality of a network. Each network identified in a new individual would have to be correlated with the template images of networks obtained in this study to determine the strongest matches. Ultimately, this classification procedure would aid in selecting networks whose activations (averaged over post-stimulus time) would then be subjected to the regression model in order to generate a pain prediction.
To address this first limitation (lack of model validation), future research may use larger datasets to re-conduct the current study with the addition of a validation protocol, or test the current model in new and independent data according to the procedure outlined above. That said, predictions based on these networks are likely to be improved if signal differences within sub-networks and regions of components are accounted for by using pattern-based analyses like MVPA, instead of constructing models based on a whole-network index of activation (i.e. estimated HDRs for entire networks).
A second limitation is that we included stimuli not rated as painful (based on the 100-point pain threshold specified by the VAS scale) in both network extraction via fMRI-CPCA and regression models of pain ratings. For network extraction, this means that networks delineated were composed of voxels that remained functionally-connected across non-painful and painful stimulation; in this way, any voxels that became incorporated into the networks—or any new networks that were formed—during painful stimuli only were potentially missed by the analysis. For regression models, it raises the possibility that networks were related to warmth more so than pain perception. This would be the case if model-based predictions of ratings below the pain threshold (i.e. warmth) were consistently better than those of ratings above the pain threshold (i.e. pain).
Conclusion
Overall, this study has contributed to neuroimaging research on pain by elucidating three functional networks evoked by thermal stimulation: a sensorimotor response network for immediate pain avoidance (Component 1), a frontoparietal attention network mobilized by salience detection processes (Component 2), and the default-mode network (Component 4). Of these, attention and default-mode networks were related to pain perception both within and between participants. From a purely technical perspective, this study validates fMRI-CPCA within the domain of pain research for the first time, highlighting advantages compared to existing approaches, including that the parcellation of multiple task-related networks is accomplished without a priori selection of regions-of-interest (i.e. no assumptions about spatial properties of networks). Moreover, fMRI-CPCA does not rely on models that assume specific HDR shapes to identify task-related activity; instead, HDRs are predicted using FIR basis functions, which simply specify an interval during which task-relevant activity is expected to occur. In this way, fMRI-CPCA detects HDRs (and potentially networks) elicited by cognitive processes that may be unaccounted for in conventional analyses.
More generally, the findings obtained provide a foundation from which to further investigate these networks, their proposed functions and their pain predictive value. The networks identified (especially the attention and default-mode networks) may have implications for pain treatments, if they can be targeted successfully with strategies based on neuromodulation (Alo & Holsheimer,
2002), behavioural therapy (Eccleston et al.,
2013), or real-time fMRI feedback (Chapin et al.,
2012), for example. Further, validated pain predictions can be generated from these networks and potentially refined by applying MVPA within network boundaries. Patterns delineated through MVPA may ultimately serve as objective measures of pain, which are of crucial importance to effective pain management in patients unable to self-report their pain.