Periodic Reporting for period 3 - PredictingPain (Deconstructing pain with predictive models: from neural architecture to pain relief)
Reporting period: 2021-10-01 to 2023-03-31
Pain is a useful and important warning signal of impending or actual tissue damage, but in its chronic form it is also a troubling healthcare issue. Chronic pain is a burden for millions of people in terms of suffering, as well as for societies in terms of costs. In Europe, about 20% of adults suffer from chronic pain, resulting in tremendous societal impact in terms of increased use of healthcare resources, decreased quality of life, and decreased ability to participate in the workforce. Thus, there is an obvious need for improved treatment that in turn calls for a better understanding of the neurobiological processes underlying pain.
It is important to point out that in many circumstances the subjectively perceived intensity of pain is not necessarily related to the amount of peripherally received nociceptive input. This suggests a strong involvement of the central nervous system (CNS) in the construction of pain, however a unifying mechanistic framework for how CNS processing shapes pain perception is still lacking. Here we aim to provide such a framework by taking a novel approach to pain that capitalizes on recent advances in neuroscience that characterize perception not as a passively arising response to sensory stimuli, but as an active process, in which the CNS constantly generates predictions about the sensory inputs it receives and adjusts these predictions in light of new sensory input.
One implementation of this general idea is termed predictive coding and has been applied especially in the visual and auditory domain over the last years, where it was able to explain numerous perceptual and neurobiological phenomena. Predictive coding rests on the idea that each area in a sensory hierarchy contains two distinct populations of neurons: ‘prediction units’ that represent the currently chosen hypothesis of sensory input and ‘prediction error units’ that represent the mismatch between the sensory input and the current hypothesis. In opposition to traditional models, which assume that sensory input is passed forward along the sensory hierarchy, predictive coding assumes that only the unexplained part of sensory input is passed forward (via prediction error units) and that it is the prediction units that contain the current hypothesis of sensory input. Together, these units are supposed to be organized in a hierarchical architecture, where prediction error units carry feedforward activity and prediction units carry feedback activity, with the goal of optimizing predictions and reducing prediction errors.
Interestingly, this framework has not yet received a large amount of attention in the context of pain or nociceptive processing and one might also question whether neurobiological processes in the nociceptive system – or the subjective experience of pain – are suitable subjects for this approach, since they are rather removed (both in terms of anatomy and experience) from the visual system, where the foundations of predictive coding were established. In this project, we will therefore investigate if basic principles of predictive coding also apply in the context of pain. We aim to investigate predictive coding mechanisms at all levels of the CNS, but will especially focus on the human spinal cord, using advanced neuroimaging techniques that should allow for novel insights into this lowest and earliest level of the nociceptive processing hierarchy. This is motivated by the dorsal horn of the spinal cord being the first station of CNS pain processing, and also a structure that is involved in pathological pain processes. Most importantly though, the spinal cord is of strategic importance, as predictive signals at this lowest level of the CNS might have a profound effect on feedforward propagation of prediction errors to higher levels and thus the ensuing pain perception.
It is important to point out that in many circumstances the subjectively perceived intensity of pain is not necessarily related to the amount of peripherally received nociceptive input. This suggests a strong involvement of the central nervous system (CNS) in the construction of pain, however a unifying mechanistic framework for how CNS processing shapes pain perception is still lacking. Here we aim to provide such a framework by taking a novel approach to pain that capitalizes on recent advances in neuroscience that characterize perception not as a passively arising response to sensory stimuli, but as an active process, in which the CNS constantly generates predictions about the sensory inputs it receives and adjusts these predictions in light of new sensory input.
One implementation of this general idea is termed predictive coding and has been applied especially in the visual and auditory domain over the last years, where it was able to explain numerous perceptual and neurobiological phenomena. Predictive coding rests on the idea that each area in a sensory hierarchy contains two distinct populations of neurons: ‘prediction units’ that represent the currently chosen hypothesis of sensory input and ‘prediction error units’ that represent the mismatch between the sensory input and the current hypothesis. In opposition to traditional models, which assume that sensory input is passed forward along the sensory hierarchy, predictive coding assumes that only the unexplained part of sensory input is passed forward (via prediction error units) and that it is the prediction units that contain the current hypothesis of sensory input. Together, these units are supposed to be organized in a hierarchical architecture, where prediction error units carry feedforward activity and prediction units carry feedback activity, with the goal of optimizing predictions and reducing prediction errors.
Interestingly, this framework has not yet received a large amount of attention in the context of pain or nociceptive processing and one might also question whether neurobiological processes in the nociceptive system – or the subjective experience of pain – are suitable subjects for this approach, since they are rather removed (both in terms of anatomy and experience) from the visual system, where the foundations of predictive coding were established. In this project, we will therefore investigate if basic principles of predictive coding also apply in the context of pain. We aim to investigate predictive coding mechanisms at all levels of the CNS, but will especially focus on the human spinal cord, using advanced neuroimaging techniques that should allow for novel insights into this lowest and earliest level of the nociceptive processing hierarchy. This is motivated by the dorsal horn of the spinal cord being the first station of CNS pain processing, and also a structure that is involved in pathological pain processes. Most importantly though, the spinal cord is of strategic importance, as predictive signals at this lowest level of the CNS might have a profound effect on feedforward propagation of prediction errors to higher levels and thus the ensuing pain perception.
We have designed several experiments that allow us to investigate the relevance of predictions (or, as they are often also referred to: expectations) and the underlying neural signals from different perspectives. This is done for example by experimentally manipulating i) the temporal structure of pain stimulation (e.g. one pain stimulus predicting the occurrence of another one) or ii) the probability by which painful stimuli will occur in a certain context. With regards to the former example (temporal structure of painful stimulation), we have replicated the known finding that pain-related cortical electroencephalography (EEG) responses are reduced for a temporally-predictable second painful stimulus compared to a first painful stimulus and are currently investigating to which degree this depends on the expectations that participants form about this relationship. With regards to the latter example, i.e. the association between a context and the probability of receiving pain, we were able to demonstrate that contexts with a high likelihood of pain delivery resulted in a more intense experience of pain, despite no change in the actual stimulation. The expectations that participants generated in these contexts were also evident in metrics that index autonomic nervous system activation, such as changes in sweating (skin conductance responses) and pupil size (pupil dilation responses): higher probabilities of receiving pain resulted in higher skin conductance and higher pupil dilation responses to pain, despite the physical stimulus intensity delivered to the skin of participants being identical across these conditions. Interestingly, while we were thus able to clearly demonstrate the behavioural coding of expectation/prediction signals, we were so far not able to obtain any evidence for a behavioural representation of prediction errors, i.e. responses that are driven by a mismatch between the predicted and the actual occurrence of painful stimulation.
In addition, we have carried out several methodological projects in order to improve functional magnetic resonance imaging (fMRI) data quality from the human spinal cord, both at the field strength of 3 Tesla (which is the current standard for spinal cord fMRI) and at the field strength of 7 Tesla (which is needed in order to obtain high-resolution fMRI data). For example, at 3 Tesla we have devised a method that allows for an automated calculation of so-called ‘shim-settings’ which make the magnetic field more homogenous and contribute to better data quality. At 7 Tesla, we have developed several protocols for obtaining high-resolution fMRI data, with which we aim to separate prediction and prediction error signals in the human spinal cord both spatially and temporally.
In addition, we have carried out several methodological projects in order to improve functional magnetic resonance imaging (fMRI) data quality from the human spinal cord, both at the field strength of 3 Tesla (which is the current standard for spinal cord fMRI) and at the field strength of 7 Tesla (which is needed in order to obtain high-resolution fMRI data). For example, at 3 Tesla we have devised a method that allows for an automated calculation of so-called ‘shim-settings’ which make the magnetic field more homogenous and contribute to better data quality. At 7 Tesla, we have developed several protocols for obtaining high-resolution fMRI data, with which we aim to separate prediction and prediction error signals in the human spinal cord both spatially and temporally.
We believe that the methodological approach we have developed in order to investigate pain-related responses in the human spinal cord is currently unique in terms of its ability to resolve fine-scale responses: we are using non-invasive spinal fMRI with a spatial resolution of 750 x 750 micrometres. When relating this to known anatomical data of spinal cord organization, it becomes clear that with this resolution we are in a spatial range that allows for clearly differentiating the functionally relevant units, i.e. the laminae or layers of the spinal cord. As a first step, we will differentiate responses in superficial layers (which show a preference towards selectively responding to painful stimuli) and deep layers (which also respond to non-painful stimulation of the skin), but at later stages of the project we also hope to be able to even differentiate responses within the superficial layers (e.g. in terms of populations of nerve cells that project towards the brain compared to those that primarily make connections within the spinal cord).
We expect that within the time-frame of this project we will be able to make use of this and other methodological developments to provide an answer to the question of whether fundamental properties of the predictive coding framework also apply in the domain of pain. Importantly, we approach this from several different perspectives in order to probe various aspects of prediction and prediction error signals. One further aim is to not only probe the neural domain, but to also link the ensuing results with the subjective experience of pain, i.e. to bridge these two levels. In a last step, we will expand this approach from the domain of pain to the – clinically at least as relevant – domain of pain relief: here, we will also be investigating to what degree predictive processes might be impaired in patients with chronic pain.
We expect that within the time-frame of this project we will be able to make use of this and other methodological developments to provide an answer to the question of whether fundamental properties of the predictive coding framework also apply in the domain of pain. Importantly, we approach this from several different perspectives in order to probe various aspects of prediction and prediction error signals. One further aim is to not only probe the neural domain, but to also link the ensuing results with the subjective experience of pain, i.e. to bridge these two levels. In a last step, we will expand this approach from the domain of pain to the – clinically at least as relevant – domain of pain relief: here, we will also be investigating to what degree predictive processes might be impaired in patients with chronic pain.