Evoked pain intensity representation is distributed across brain systems: A multistudy mega-analysis
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Abstract
Information is coded in the brain at different scales for different phenomena: locally, distributed across regions and networks, and globally. For pain, the scale of representation is controversial. Although generally believed to be an integrated cognitive and sensory phenomenon implicating diverse brain systems, quantitative characterizations of which regions and networks are sufficient to represent pain are lacking. In this meta-analysis (or mega-analysis) using data from 289 participants across 10 studies, we use model comparison combined with multivariate predictive models to investigate the spatial scale and location of acute pain representation. We compare models based on (a) a single most pain-predictive module, either previously identified elementary regions or a single best large-scale cortical resting-state network module; (b) selected cortical-subcortical systems related to evoked pain in prior literature (‘multi-system models’); and (c) a model spanning the full brain. We estimate the accuracy of pain intensity predictions using cross validation (7 studies) and subsequently validate in three independent holdout studies. All spatial scales convey information about pain intensity, but distributed, multi-system models better characterize pain representations than any individual region or network (e.g. multisystem models explain >20% more of individual subject pain ratings than the best elementary region). Full brain models showed no predictive advantage over multi-system models. These findings quantify the extent that representation of evoked pain experience is distributed across multiple cortical and subcortical systems, show that pain representation is not circumscribed by any elementary region or conical network, and provide a blueprint for identifying the spatial scale of information in other domains.
Significance Statement
We define modular, multisystem and global views of brain function, use multivariate fMRI decoding to characterize pain representations at each level, and provide evidence for a multisystem representation of evoked pain. We further show that local views necessarily exclude important components of pain representation, while a global full brain representation is superfluous, even though both are viable frameworks for representing pain. These findings quantitatively juxtapose and reconcile divergent conclusions from evoked pain studies within a generalized neuroscientific framework, and provide a blueprint for investigating representational architecture for diverse brain processes.
Author Note
Data storage supported by the University of Colorado Boulder “PetaLibrary”. Research funded by NIMH R01 MH076136, NIDA R01 DA046064 and NIDA R01 DA035484. Lauren Atlas is supported in part by funding from the Intramural Research Program of the National Center for Complementary and Integrative Health, National Institutes of Health (ZIA-AT000030). Marina Lopez-Sola is supported by a Serra Hunter fellow lecturer program. We would like to thank Dr. Christian Buchel for contributing data to this project, and Dr. Marta Čeko for comments and feedback on the manuscript.
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Summary: The reviewers felt that the idea that the pain estimation was a magnitude estimation of heat (even heat pain) could not be ruled out. One of the beauties of the pain percept is the ability to reach the same percept with a large variety of stimulus modalities and this was not done in this ms. So there is nothing to disabuse one of the idea that this is heat or even heat pain but not pain per se. The reviewers also were concerned that the variabilities of the studies included and the individuals therein were ignored: R/L, location and of course manipulation. Finally, it is not an automatic that every individual accomplishes pain perception in the same way. Thus while individual variability may undermine the reliability of the results, it could also reflect a biological possibility, one which the authors do not address.
Reviewer #1:
Summary: The reviewers felt that the idea that the pain estimation was a magnitude estimation of heat (even heat pain) could not be ruled out. One of the beauties of the pain percept is the ability to reach the same percept with a large variety of stimulus modalities and this was not done in this ms. So there is nothing to disabuse one of the idea that this is heat or even heat pain but not pain per se. The reviewers also were concerned that the variabilities of the studies included and the individuals therein were ignored: R/L, location and of course manipulation. Finally, it is not an automatic that every individual accomplishes pain perception in the same way. Thus while individual variability may undermine the reliability of the results, it could also reflect a biological possibility, one which the authors do not address.
Reviewer #1:
This meta-analysis aims to resolve once and for all the debate surrounding how pain is represented in the brain. The authors take us one step closer, finding that multi-system and whole brain models outperform modular (single locales or single networks) models. They do not see an advantage of the whole brain model over multi-system possibilities. However, as they explain in the Discussion, this may be due to technical liabilities in the evaluation of whole brain models.
A major concern is that all of the studies used thermal stimulation. Then, in contrast to this homogeneity in stimulus, the manipulations varied widely but did not include straight up vicarious pain. It would seem that if pain report is the variable trying to be explained, studies without a somatosensory stimulus would be particularly informative.
One other comment. An underlying assumption here is that individuals use the same brain circuits to interpret and report pain. This may not be warranted. Certainly, in reductive systems where this can be and has been rigorously studied (eg. stomatogastric ganglion), a consistent finding has been that different individuals reach the same endpoint using different circuit mechanisms.
Reviewer #2:
The authors tackle an important topic, namely the scale at which pain is represented in the human brain, based on fmri brain activity collected in 7 studies and in more than 300 subjects. The statistical approach seems robust and adequate as more than 45 different models compete with each other. However, the study completely lacks any controls and remains questionable if they are actually modeling pain or simply magnitude evaluation. Main concerns are further expounded below:
The study is based on a convenience data set and as such is not designed to properly address the question.
Although the authors purport to model pain perception, in fact they are simply modeling the evaluation of the magnitude of a stimulus, which may not even be painful in the lowest quartile of the magnitudes attempted to be predicted. Thus, the study lacks the critical control of a simple task of magnitude estimation. It is quite likely that the extended brain regions and networks identified are all related to magnitude assessment rather than pain perception.
Additionally one would need to see a contrast between nociceptive stimuli and at least one other sensory modality, for example touch, to demonstrate that the observed required networks are in fact specific to pain rather than to any other sensation.
The diversity of the data sets remains worrisome as they most likely are simply adding to unaccounted variance.
The report remains far too technical and does not convince the reader that they have properly untangled this complex issue at hand.
Reviewer #3:
General assessment:
The manuscript is well written and the results were clearly presented. The methods details of this study remain one of the most comprehensive amongst fMRI MVPA papers, and the statistical procedures taken to ensure validity of the models would be an extensive guide for similar future studies. However, as the methods section was fairly dense, the narrative of the article can be difficult to follow at times. Overall, the manuscript will be of interest and relevant to readers.
Concerns:
Despite the large sample size and careful statistical validation, the data preparation step of this study, in particular, the decision to average GLM trial brain maps within pain intensity quartile within individuals, may cast some doubts on the conclusions. While this step was necessary for computational tractability, it effectively reduced each participant's data into four brain maps for model training (Figure 2B-C). As far as I understand, this manipulation is likely to smooth out most effects contributed by non-temperature experimental factors due to trial permutation. In addition, it further reduces the temporal resolution of evoked pain into `snapshots' of several pain intensities. While the remainder of the study carefully compared modular and multisystem representations of pain, the study seems incomplete without discussing how this data manipulation might impact the conclusions, or how resulting biases can be acknowledged and mitigated. For example, modular representation of pain could be the superior representation in a particular cognitive manipulation paradigm for pain, or a specific time window/point during extended pain experience, and these possibilities cannot be excluded based on present evidence.
In addition, as mentioned by the authors, between-subject variance is not considered in the present analysis, which appeared to contribute a large amount of pain intensity variance (Figure 1B). It would be great if the authors can discuss the implication of the results in such context, and how MVPA methods can be used to study those effects.
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