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Modeling Pain Using Fmri: from Regions to Biomarkers

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Modeling Pain Using Fmri: from Regions to Biomarkers Neurosci. Bull. www.neurosci.cn DOI 10.1007/s12264-017-0150-1 www.springer.com/12264 REVIEW Modeling Pain Using fMRI: From Regions to Biomarkers 1 1 Marianne C. Reddan • Tor D. Wager Received: 25 March 2017 / Accepted: 2 June 2017 Ó Shanghai Institutes for Biological Sciences, CAS and Springer Nature Singapore Pte Ltd. 2017 Abstract Pain is a subjective and complex phenomenon. Keywords Pain Á Biomarkers Á fMRI Á Models Á Machine Its complexity is related to its heterogeneity: multiple learning component processes, including sensation, affect, and cognition, contribute to pain experience and reporting. These components are likely to be encoded in distributed Introduction brain networks that interact to create pain experience and pain-related decision-making. Therefore, to understand Pain is a subjective phenomenon that is related to, but not pain, we must identify these networks and build models reducible to, nociceptive signaling of actual or potential of these interactions that yield testable predictions about tissue damage [1]. Indeed, pain is a complex psychological pain-related outcomes. We have developed several such manifestation of interactions among multiple component models or ‘signatures’ of pain, by (1) integrating activity processes, including nociception (i.e., neurophysiology, across multiple systems, and (2) using pattern-recognition sensation), cognitive appraisals (i.e., expectation, framing), to identify processes related to pain experience. One and affect and valuation [2–8]. model, the Neurologic Pain Signature, is sensitive and Physicians rely primarily on patient self-reports to specific to pain in individuals, involves brain regions that diagnose and treat pain; however, self-report is subject to receive nociceptive afferents, and shows little effect of limitations in self-perception and metacognition, and may expectation or self-regulation in tests to date. Another, the not provide insight into the cause of pain or how it should ‘Stimulus Intensity-Independent Pain Signature’, explains be treated [9–13]. Indeed, patients are sometimes unable to substantial additional variation in trial-to-trial pain reports. adequately describe their pain. For example, a study It involves many brain regions that do not show increased comparing dementia patients and controls found that facial activity in proportion to noxious stimulus intensity, includ- expressions, but not verbal reports, scaled strongly with ing medial and lateral prefrontal cortex, nucleus accum- noxious stimulus intensity [14]. Furthermore, self-report bens, and hippocampus. Responses in this system mediate conveys limited information about the sources and mech- expectancy and perceived control effects in several studies. anisms of pain. Overall, this approach provides a pathway to understanding Pain experience is constructed in the brain, and can pain by identifying multiple systems that track different result from dysregulated brain circuits. In some cases, aspects of pain. Such componential models can be normally innocuous sensory signals or low levels of combined in unique ways on a subject-by-subject basis to nociceptive input may be enhanced and transformed within explain an individual’s pain experience. the brain to create pain. Pain can exist independent of local peripheral nociceptive input; for example, phantom limb pain persists long after the amputation, even under spinal & Tor D. Wager anesthesia [15, 16]. Even in cases where nociceptive input [email protected] is required to maintain pain, the brain interprets that input 1 Department of Psychology and Neuroscience, University of and constructs pain experience by combining it with other Colorado Boulder, Boulder, CO 80303, USA signals. For example, nerve injury models of chronic pain 123 Neurosci. Bull. involve neuroinflammation in the spinal cord, including cingulate cortex (ACC), posterior cingulate cortex (PCC), cytokines and chemokines produced by spinal glial cells, insula (Ins), amygdala, primary and secondary somatosen- which are thought to mediate persistent pain [17, 18]. sory cortices (S1 and S2), and the periaqueductal gray These changes may require input from peripheral neurons (PAG) [4, 30, 31]. Activation in these regions is associated [19], but once sensitization has occurred, persistent sensi- with reports of increases in pain [32]. Human electrophys- tization in nociceptive circuits can drive pain with normal iological studies and animal models further corroborate the sensory input. Sensitization can also occur in supra-spinal involvement of these brain regions in nociception [33–35]. brain circuits in the thalamus [20] and amygdala [21]. One problem with standard neuroimaging approaches is Thus, understanding pain in a living individual will that the pain matrix is a general concept, not a precise ultimately require understanding the neurophysiology of theory: it can be activated by touch [36], cognitive demand the supra-spinal brain processes that create it. Both [37], and to some degree, it can be activated even in those limitations in self-report and the cerebral nature of pain who lack nociceptive input [38]. Therefore, activation in construction make it desirable to develop models of how the ‘pain matrix’ during noxious stimulation is not the human brain constructs pain experience. necessarily specific to pain [39, 40]. Neuroimaging allows for a systems-level approach to A second problem is that many other brain regions play the brain representation of pain [22–24], and can be used to important roles in shaping various aspects of pain experi- develop predictive biomarkers for component processes ence and behavior. Descending modulatory pain systems related to pain [25]. A neuroimaging biomarker is a interact with nociceptive signaling in complex ways measurable pattern of brain activity predictive of some (Fig. 1) that vary across individuals and contexts outcome, such as pain self-report. Calling a brain measure [41–44]. For example, pathways involving connections a ‘‘pain biomarker’’ does not imply that it is perfectly between the PFC and nucleus accumbens (NAc) shape pain predictive or highly specific to pain, or that it is a complete behavior in important ways [45, 46]—but which aspects of model of pain. These issues are empirical ones, which pain behavior remains less clear. These pathways may require extended study of precisely defined markers across interact with primary nociception [47] in some cases, but studies and laboratories. Imaging-based biomarkers have influence motivation and avoidance independent of noci- the potential to (1) complement self-report measures in ception in others [48–50]. This modulatory system is multi-modal assessments [26]; (2) characterize the norma- tive systems that give rise to pain and abnormalities in patients [25]; and (3) provide biological targets for pharmacological, psychological, and neuromodulatory interventions [27, 28]. Critically, even if behavioral measures of pain and other diagnostic criteria are available and trusted, biomarkers can help establish an understanding of the neurological basis for the symptoms. Showing that a brain measure strongly tracks symptoms strengthens evi- dence that brain features are disease-relevant. This review discusses how the biomarker approach to pain neuroimag- ing differs from standard brain mapping methods, and reviews progress to date in using this new approach to understand and assess pain. (For free Matlab-based soft- ware tools for creating predictive models with neuroimag- ing data, visit our code repositories: https://github.com/ canlab). Fig. 1 The ‘‘Pain Matrix’’ and its Descending Modulation. Simpli- fied overview of the brain targets of the ascending (green) and Pain Neuroimaging: Traditional and New descending (yellow) modulatory pathways for pain. The ‘‘pain Approaches matrix’’ is comprised of the thalamus, anterior cingulate cortex (ACC), the anterior and dorsal posterior insula (aINS and dpINS), primary and secondary somatosensory cortices (S1 and S2) and the Pain neuroimaging has, until recently, been dominated by periaqueductal gray (PAG), and is often extended to include studies mapping the effects of noxious stimulation and pain prefrontal cortices (PFC), as well as the basal ganglia (BG) and modulation [29]. Focus has primarily been on the ‘pain amygdala (AMY). Neuroimaging approaches to the study of these pathways must account for the complex interactions between these matrix’, a general set of brain regions responsive to regions and the multiple roles any one region may play in pain noxious stimuli that includes the thalamus, anterior perception. 123 M. C. Reddan, T. D. Wager: Pain Models further complicated by evidence that the PFC may be pain with greater activity, are found in structures frequently involved in both pain regulation [51] and pain catastro- deactivated by pain, such as the ventromedial PFC and phizing [52, 53], suggesting a direct role in pain avoidance. precuneus (Fig. 2C). However, unlike the pain matrix, the Modeling of brain connectivity between the ventromedial NPS is a predictive pattern of activity, making it a single, PFC and PAG during pain avoidance learning also points integrative model of evoked pain. In tests to date, the NPS to a critical role in avoidance beyond nociception [54]. The successfully predicts pain evoked by noxious events, with relationship between PFC and the NAc is just one example 90%–100% accuracy as long as the stimulation is clearly of a component process involved in the pain experience judged as painful
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