Multimodal physiological sensing for the assessment of acute pain

Pain assessment is a challenging task encountered by clinicians. In clinical settings, patients' self-report is considered the gold standard in pain assessment. However, patients who are unable to self-report pain are at a higher risk of undiagnosed pain. In the present study, we explore the use of multiple sensing technologies to monitor physiological changes that can be used as a proxy for objective measurement of acute pain. Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) signals were collected from 22 participants under two pain intensities (low and high) and on two different anatomical locations (forearm and hand). Three machine learning models were implemented, including support vector machines (SVM), decision trees (DT), and linear discriminant analysis (LDA) for the identification of pain. Various pain scenarios were investigated, identification of pain (no pain, pain), multiclass (no pain, low pain, high pain), and identification of pain location (forearm, hand). Reference classification results from individual sensors and from all sensors together were obtained. After feature selection, results showed that EDA was the most informative sensor in the three pain conditions, 93.2±8% in identification of pain, 68.9±10% in the multiclass problem, and 56.0±8% for the identification of pain location. These results identify EDA as the superior sensor in our experimental conditions. Future work is required to validate the obtained features to improve its feasibility in more realistic scenarios. Finally, this study proposes EDA as a candidate to design a tool that can assist clinicians in the assessment of acute pain of nonverbal patients.

Post-injury pain and behaviour: a control theory perspective

Injuries of various types occur commonly in the lives of humans and other animals and lead to a pattern of persistent pain and recuperative behaviour that allows safe and effective recovery. In this Perspective, we propose a control-theoretic framework to explain the adaptive processes in the brain that drive physiological post-injury behaviour. We set out an evolutionary and ethological view on how animals respond to injury, illustrating how the behavioural state associated with persistent pain and recuperation may be just as important as phasic pain in ensuring survival. Adopting a normative approach, we suggest that the brain implements a continuous optimal inference of the current state of injury from diverse sensory and physiological signals. This drives the various effector control mechanisms of behavioural homeostasis, which span the modulation of ongoing motivation and perception to drive rest and hyper-protective behaviours. However, an inherent problem with this is that these protective behaviours may partially obscure information about whether injury has resolved. Such information restriction may seed a tendency to aberrantly or persistently infer injury, and may thus promote the transition to pathological chronic pain states.

Improving sleep and learning in rehabilitation after stroke, part 2 (INSPIRES2): study protocol for a home-based randomised control trial of digital cognitive behavioural therapy (dCBT) for insomnia

INTRODUCTION

Consolidation of motor skill learning, a key component of rehabilitation post-stroke, is known to be sleep dependent. However, disrupted sleep is highly prevalent after stroke and is often associated with poor motor recovery and quality of life. Previous research has shown that digital cognitive behavioural therapy (dCBT) for insomnia can be effective at improving sleep quality after stroke. Therefore, the aim of this trial is to evaluate the potential for sleep improvement using a dCBT programme, to improve rehabilitation outcomes after stroke.

METHODS AND ANALYSIS

We will conduct a parallel-arm randomised controlled trial of dCBT (Sleepio) versus treatment as usual among individuals following stroke affecting the upper limb. Up to 100 participants will be randomly allocated (2:1) into either the intervention (6-8 week dCBT) or control (continued treatment as usual) group. The primary outcome of the study will be change in insomnia symptoms pre to post intervention compared with treatment as usual. Secondary outcomes include improvement in overnight motor memory consolidation and sleep measures between intervention groups, correlations between changes in sleep behaviour and overnight motor memory consolidation in the dCBT group and changes in symptoms of depression and fatigue between the dCBT and control groups. Analysis of covariance models and correlations will be used to analyse data from the primary and secondary outcomes.

ETHICS AND DISSEMINATION

The study has received approval from the National Research Ethics Service (22/EM/0080), Health Research Authority (HRA) and Health and Care Research Wales (HCRW), IRAS ID: 306 291. The results of this trial will be disseminated via presentations at scientific conferences, peer-reviewed publication, public engagement events, stakeholder organisations and other forms of media where appropriate.

TRIAL REGISTRATION NUMBER

NCT05511285.

Evidence for dopaminergic involvement in endogenous modulation of pain relief

Relief of ongoing pain is a potent motivator of behavior, directing actions to escape from or reduce potentially harmful stimuli. Whereas endogenous modulation of pain events is well characterized, relatively little is known about the modulation of pain relief and its corresponding neurochemical basis. Here, we studied pain modulation during a probabilistic relief-seeking task (a 'wheel of fortune' gambling task), in which people actively or passively received reduction of a tonic thermal pain stimulus. We found that relief perception was enhanced by active decisions and unpredictability, and greater in high novelty-seeking trait individuals, consistent with a model in which relief is tuned by its informational content. We then probed the roles of dopaminergic and opioidergic signaling, both of which are implicated in relief processing, by embedding the task in a double-blinded cross-over design with administration of the dopamine precursor levodopa and the opioid receptor antagonist naltrexone. We found that levodopa enhanced each of these information-specific aspects of relief modulation but no significant effects of the opioidergic manipulation. These results show that dopaminergic signaling has a key role in modulating the perception of pain relief to optimize motivation and behavior.

Confidence of probabilistic predictions modulates the cortical response to pain

Pain typically evolves over time, and the brain needs to learn this temporal evolution to predict how pain is likely to change in the future and orient behavior. This process is termed temporal statistical learning (TSL). Recently, it has been shown that TSL for pain sequences can be achieved using optimal Bayesian inference, which is encoded in somatosensory processing regions. Here, we investigate whether the confidence of these probabilistic predictions modulates the EEG response to noxious stimuli, using a TSL task. Confidence measures the uncertainty about the probabilistic prediction, irrespective of its actual outcome. Bayesian models dictate that the confidence about probabilistic predictions should be integrated with incoming inputs and weight learning, such that it modulates the early components of the EEG responses to noxious stimuli, and this should be captured by a negative correlation: when confidence is higher, the early neural responses are smaller as the brain relies more on expectations/predictions and less on sensory inputs (and vice versa). We show that participants were able to predict the sequence transition probabilities using Bayesian inference, with some forgetting. Then, we find that the confidence of these probabilistic predictions was negatively associated with the amplitude of the N2 and P2 components of the vertex potential: the more confident were participants about their predictions, the smaller the vertex potential. These results confirm key predictions of a Bayesian learning model and clarify the functional significance of the early EEG responses to nociceptive stimuli, as being implicated in confidence-weighted statistical learning.

Neurofeedback Training without Explicit Phantom Hand Movements and Hand-Like Visual Feedback to Modulate Pain: A Randomized Crossover Feasibility Trial

Phantom limb pain is attributed to abnormal sensorimotor cortical representations, although the causal relationship between phantom limb pain and sensorimotor cortical representations suffers from the potentially confounding effects of phantom hand movements. We developed neurofeedback training to change sensorimotor cortical representations without explicit phantom hand movements or hand-like visual feedback. We tested the feasibility of neurofeedback training in fourteen patients with phantom limb pain. Neurofeedback training was performed in a single-blind, randomized, crossover trial using two decoders constructed using motor cortical currents measured during phantom hand movements; the motor cortical currents contralateral or ipsilateral to the phantom hand (contralateral and ipsilateral training) were estimated from magnetoencephalograms. Patients were instructed to control the size of a disk, which was proportional to the decoding results, but to not move their phantom hands or other body parts. The pain assessed by the visual analogue scale was significantly greater after contralateral training than after ipsilateral training. Classification accuracy of phantom hand movements significantly increased only after contralateral training. These results suggested that the proposed neurofeedback training changed phantom hand representation and modulated pain without explicit phantom hand movements or hand-like visual feedback, thus showing the relation between the phantom hand representations and pain. PERSPECTIVE: Our work demonstrates the feasibility of using neurofeedback training to change phantom hand representation and modulate pain perception without explicit phantom hand movements and hand-like visual feedback. The results enhance the mechanistic understanding of certain treatments, such as mirror therapy, that change the sensorimotor cortical representation.

Computational and neural mechanisms of statistical pain learning

Pain invariably changes over time. These fluctuations contain statistical regularities which, in theory, could be learned by the brain to generate expectations and control responses. We demonstrate that humans learn to extract these regularities and explicitly predict the likelihood of forthcoming pain intensities in a manner consistent with optimal Bayesian inference with dynamic update of beliefs. Healthy participants received probabilistic, volatile sequences of low and high-intensity electrical stimuli to the hand during brain fMRI. The inferred frequency of pain correlated with activity in sensorimotor cortical regions and dorsal striatum, whereas the uncertainty of these inferences was encoded in the right superior parietal cortex. Unexpected changes in stimulus frequencies drove the update of internal models by engaging premotor, prefrontal and posterior parietal regions. This study extends our understanding of sensory processing of pain to include the generation of Bayesian internal models of the temporal statistics of pain.

A multi-site, multi-disorder resting-state magnetic resonance image database

Machine learning classifiers for psychiatric disorders using resting-state functional magnetic resonance imaging (rs-fMRI) have recently attracted attention as a method for directly examining relationships between neural circuits and psychiatric disorders. To develop accurate and generalizable classifiers, we compiled a large-scale, multi-site, multi-disorder neuroimaging database. The database comprises resting-state fMRI and structural images of the brain from 993 patients and 1,421 healthy individuals, as well as demographic information such as age, sex, and clinical rating scales. To harmonize the multi-site data, nine healthy participants (“traveling subjects”) visited the sites from which the above datasets were obtained and underwent neuroimaging with 12 scanners. All participants consented to having their data shared and analyzed at multiple medical and research institutions participating in the project, and 706 patients and 1,122 healthy individuals consented to having their data disclosed. Finally, we have published four datasets: 1) the SRPBS Multi-disorder Connectivity Dataset 2), the SRPBS Multi-disorder MRI Dataset (restricted), 3) the SRPBS Multi-disorder MRI Dataset (unrestricted), and 4) the SRPBS Traveling Subject MRI Dataset.

Measurement(s)	mental or behavioural disorder • brain measurement • Demographic Data
Technology Type(s)	functional magnetic resonance imaging • magnetic resonance imaging • Resting State Functional Connectivity Magnetic Resonance Imaging
Factor Type(s)	age • sex • site • disorder
Sample Characteristic - Organism	Homo sapiens

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14716329

Hierarchical models of pain: Inference, information-seeking, and adaptive control

Computational models of pain consider how the brain processes nociceptive information and allow mapping neural circuits and networks to cognition and behaviour. To date, they have generally have assumed two largely independent processes: perceptual inference, typically modelled as an approximate Bayesian process, and action control, typically modelled as a reinforcement learning process. However, inference and control are intertwined in complex ways, challenging the clarity of this distinction. Here, we consider how they may comprise a parallel hierarchical architecture that combines inference, information-seeking, and adaptive value-based control. This sheds light on the complex neural architecture of the pain system, and takes us closer to understanding from where pain 'arises' in the brain.

Pain Control by Co-adaptive Learning in a Brain-Machine Interface

Innovation in the field of brain-machine interfacing offers a new approach to managing human pain. In principle, it should be possible to use brain activity to directly control a therapeutic intervention in an interactive, closed-loop manner. But this raises the question as to whether the brain activity changes as a function of this interaction. Here, we used real-time decoded functional MRI responses from the insula cortex as input into a closed-loop control system aimed at reducing pain and looked for co-adaptive neural and behavioral changes. As subjects engaged in active cognitive strategies orientated toward the control system, such as trying to enhance their brain activity, pain encoding in the insula was paradoxically degraded. From a mechanistic perspective, we found that cognitive engagement was accompanied by activation of the endogenous pain modulation system, manifested by the attentional modulation of pain ratings and enhanced pain responses in pregenual anterior cingulate cortex and periaqueductal gray. Further behavioral evidence of endogenous modulation was confirmed in a second experiment using an EEG-based closed-loop system. Overall, the results show that implementing brain-machine control systems for pain induces a parallel set of co-adaptive changes in the brain, and this can interfere with the brain signals and behavior under control. More generally, this illustrates a fundamental challenge of brain decoding applications-that the brain inherently adapts to being decoded, especially as a result of cognitive processes related to learning and cooperation. Understanding the nature of these co-adaptive processes informs strategies to mitigate or exploit them.

Reward Enhances Pain Discrimination in Humans

The notion that reward inhibits pain is a well-supported observation in both humans and animals, allowing suppression of pain reflexes to acquired rewarding stimuli. However, a blanket inhibition of pain by reward would also impair pain discrimination. In contrast, early counterconditioning experiments implied that reward might actually spare pain discrimination. To test this hypothesis, we investigated whether discriminative performance was enhanced or inhibited by reward. We found in adult human volunteers (N = 25) that pain-based discriminative ability is actually enhanced by reward, especially when reward is directly contingent on discriminative performance. Drift-diffusion modeling shows that this relates to an augmentation of the underlying sensory signal strength and is not merely an effect of decision bias. This enhancement of sensory-discriminative pain-information processing suggests that whereas reward can promote reward-acquiring behavior by inhibition of pain in some circumstances, it can also facilitate important discriminative information of the sensory input when necessary.

Corrigendum: An Evolutionarily Threat-Relevant Odor Strengthens Human Fear Memory

[This corrects the article DOI: 10.3389/fnins.2020.00255.].

BCI training to move a virtual hand reduces phantom limb pain: A randomized crossover trial

OBJECTIVE

To determine whether training with a brain-computer interface (BCI) to control an image of a phantom hand, which moves based on cortical currents estimated from magnetoencephalographic signals, reduces phantom limb pain.

METHODS

Twelve patients with chronic phantom limb pain of the upper limb due to amputation or brachial plexus root avulsion participated in a randomized single-blinded crossover trial. Patients were trained to move the virtual hand image controlled by the BCI with a real decoder, which was constructed to classify intact hand movements from motor cortical currents, by moving their phantom hands for 3 days ("real training"). Pain was evaluated using a visual analogue scale (VAS) before and after training, and at follow-up for an additional 16 days. As a control, patients engaged in the training with the same hand image controlled by randomly changing values ("random training"). The 2 trainings were randomly assigned to the patients. This trial is registered at UMIN-CTR (UMIN000013608).

RESULTS

VAS at day 4 was significantly reduced from the baseline after real training (mean [SD], 45.3 [24.2]-30.9 [20.6], 1/100 mm; p = 0.009 < 0.025), but not after random training (p = 0.047 > 0.025). Compared to VAS at day 1, VAS at days 4 and 8 was significantly reduced by 32% and 36%, respectively, after real training and was significantly lower than VAS after random training (p < 0.01).

CONCLUSION

Three-day training to move the hand images controlled by BCI significantly reduced pain for 1 week.

CLASSIFICATION OF EVIDENCE

This study provides Class III evidence that BCI reduces phantom limb pain.

An Evolutionarily Threat-Relevant Odor Strengthens Human Fear Memory

Olfaction is an evolutionary ancient sense, but it remains unclear to what extent it can influence routine human behavior. We examined whether a threat-relevant predator odor (2-methyl-2-thiazoline) would contextually enhance the formation of human fear memory associations. Participants who learned to associate visual stimuli with electric shock in this predator odor context later showed stronger fear responses to the visual stimuli than participants who learned in an aversiveness-matched control odor context. This effect generalized to testing in another odor context, even after extinction training. Results of a separate experiment indicate that a possible biological mechanism for this effect may be increased cortisol levels in a predator odor context. These results suggest that innate olfactory processes can play an important role in human fear learning. Modulatory influences of odor contexts may partly explain the sometimes maladaptive persistence of human fear memory, e.g., in post-traumatic stress disorders.

Primary functional brain connections associated with melancholic major depressive disorder and modulation by antidepressants

The limited efficacy of available antidepressant therapies may be due to how they affect the underlying brain network. The purpose of this study was to develop a melancholic MDD biomarker to identify critically important functional connections (FCs), and explore their association to treatments. Resting state fMRI data of 130 individuals (65 melancholic major depressive disorder (MDD) patients, 65 healthy controls) were included to build a melancholic MDD classifier, and 10 FCs were selected by our sparse machine learning algorithm. This biomarker generalized to a drug-free independent cohort of melancholic MDD, and did not generalize to other MDD subtypes or other psychiatric disorders. Moreover, we found that antidepressants had a heterogeneous effect on the identified FCs of 25 melancholic MDDs. In particular, it did impact the FC between left dorsolateral prefrontal cortex (DLPFC)/inferior frontal gyrus (IFG) and posterior cingulate cortex (PCC)/precuneus, ranked as the second 'most important' FC based on the biomarker weights, whilst other eight FCs were normalized. Given that left DLPFC has been proposed as an explicit target of depression treatments, this suggest that the limited efficacy of antidepressants might be compensated by combining therapies with targeted treatment as an optimized approach in the future.

Pain: A Precision Signal for Reinforcement Learning and Control

Since noxious stimulation usually leads to the perception of pain, pain has traditionally been considered sensory nociception. But its variability and sensitivity to a broad array of cognitive and motivational factors have meant it is commonly viewed as inherently imprecise and intangibly subjective. However, the core function of pain is motivational-to direct both short- and long-term behavior away from harm. Here, we illustrate that a reinforcement learning model of pain offers a mechanistic understanding of how the brain supports this, illustrating the underlying computational architecture of the pain system. Importantly, it explains why pain is tuned by multiple factors and necessarily supported by a distributed network of brain regions, recasting pain as a precise and objectifiable control signal.

A prediction model of working memory across health and psychiatric disease using whole-brain functional connectivity

Working memory deficits are present in many neuropsychiatric diseases with diagnosis-related severity. However, it is unknown whether this common behavioral abnormality is a continuum explained by a neural mechanism shared across diseases or a set of discrete dysfunctions. Here, we performed predictive modeling to examine working memory ability (WMA) as a function of normative whole-brain connectivity across psychiatric diseases. We built a quantitative model for letter three-back task performance in healthy participants, using resting state functional magnetic resonance imaging (rs-fMRI). This normative model was applied to independent participants (N = 965) including four psychiatric diagnoses. Individual’s predicted WMA significantly correlated with a measured WMA in both healthy population and schizophrenia. Our predicted effect size estimates on WMA impairment were comparable to previous meta-analysis results. These results suggest a general association between brain connectivity and working memory ability applicable commonly to health and psychiatric diseases.

Classification and characterisation of brain network changes in chronic back pain: A multicenter study

Background. Chronic pain is a common, often disabling condition thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood.

Methods. We investigated brain network architecture using resting-state fMRI data in chronic back pain patients in the UK and Japan (41 patients, 56 controls), as well as open data from USA. We applied machine learning and deep learning (conditional variational autoencoder architecture) methods to explore classification of patients/controls based on network connectivity. We then studied the network topology of the data, and developed a multislice modularity method to look for consensus evidence of modular reorganisation in chronic back pain.

Results. Machine learning and deep learning allowed reliable classification of patients in a third, independent open data set with an accuracy of 63%, with 68% in cross validation of all data. We identified robust evidence of network hub disruption in chronic pain, most consistently with respect to clustering coefficient and betweenness centrality. We found a consensus pattern of modular reorganisation involving extensive, bilateral regions of sensorimotor cortex, and characterised primarily by negative reorganisation - a tendency for sensorimotor cortex nodes to be less inclined to form pairwise modular links with other brain nodes. Furthermore, these regions were found to display increased connectivity with the pregenual anterior cingulate cortex, a region known to be involved in endogenous pain control. In contrast, intraparietal sulcus displayed a propensity towards positive modular reorganisation, suggesting that it might have a role in forming modules associated with the chronic pain state.

Conclusion. The results provide evidence of consistent and characteristic brain network changes in chronic pain, characterised primarily by extensive reorganisation of the network architecture of the sensorimotor cortex.

MEG-BMI to Control Phantom Limb Pain

A brachial plexus root avulsion (BPRA) causes intractable pain in the insensible affected hands. Such pain is partly due to phantom limb pain, which is neuropathic pain occurring after the amputation of a limb and partial or complete deafferentation. Previous studies suggested that the pain was attributable to maladaptive plasticity of the sensorimotor cortex. However, there is little evidence to demonstrate the causal links between the pain and the cortical representation, and how much cortical factors affect the pain. Here, we applied lesioning of the dorsal root entry zone (DREZotomy) and training with a brain–machine interface (BMI) based on real-time magnetoencephalography signals to reconstruct affected hand movements with a robotic hand. The DREZotomy successfully reduced the shooting pain after BPRA, but a part of the pain remained. The BMI training successfully induced some plastic changes in the sensorimotor representation of the phantom hand movements and helped control the remaining pain. When the patient tried to control the robotic hand by moving their phantom hand through association with the representation of the intact hand, this especially decreased the pain while decreasing the classification accuracy of the phantom hand movements. These results strongly suggested that pain after the BPRA was partly attributable to cortical representation of phantom hand movements and that the BMI training controlled the pain by inducing appropriate cortical reorganization. For the treatment of chronic pain, we need to know how to modulate the cortical representation by novel methods.

Anterior cingulate cortex connectivity is associated with suppression of behaviour in a rat model of chronic pain

A cardinal feature of persistent pain that follows injury is a general suppression of behaviour, in which motivation is inhibited in a way that promotes energy conservation and recuperation. Across species, the anterior cingulate cortex is associated with the motivational aspects of phasic pain, but whether it mediates motivational functions in persistent pain is less clear. Using burrowing behaviour as an marker of non-specific motivated behaviour in rodents, we studied the suppression of burrowing following painful confirmatory factor analysis or control injection into the right knee joint of 30 rats (14 with pain) and examined associated neural connectivity with ultra-high-field resting state functional magnetic resonance imaging. We found that connectivity between anterior cingulate cortex and subcortical structures including hypothalamic/preoptic nuclei and the bed nucleus of the stria terminalis correlated with the reduction in burrowing behaviour observed following the pain manipulation. In summary, the findings implicate anterior cingulate cortex connectivity as a correlate of the motivational aspect of persistent pain in rodents.

Model-based and model-free pain avoidance learning

Background: While there is good evidence that reward learning is underpinned by two distinct decision control systems – a cognitive ‘model-based’ and a habitbased ‘model-free’ system, a comparable distinction for punishment avoidance has been much less clear. Methods: We implemented a pain avoidance task that placed differential emphasis on putative model-based and model-free processing, mirroring a paradigm and modelling approach recently developed for reward-based decision-making. Subjects performed a two-step decision-making task with probabilistic pain outcomes of different quantities. The delivery of outcomes was sometimes contingent on a rule signalled at the beginning of each trial, emulating a form of outcome devaluation. Results: The behavioural data showed that subjects tended to use a mixed strategy – favouring the simpler model-free learning strategy when outcomes did not depend on the rule, and favouring a model-based when they did. Furthermore, the data were well described by a dynamic transition model between the two controllers. When compared with data from a reward-based task (albeit tested in the context of the scanner), we observed that avoidance involved a significantly greater tendency for subjects to switch between model-free and model-based systems in the face of changes in uncertainty. Conclusion: Our study suggests a dual-system model of pain avoidance, similar to but possibly more dynamically flexible than reward-based decision-making.

Classification and characterisation of brain network changes in chronic back pain: A multicenter study

Background. Chronic pain is a common, often disabling condition thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Methods. We investigated brain network architecture using resting-state fMRI data in chronic back pain patients in the UK and Japan (41 patients, 56 controls), as well as open data from USA. We applied machine learning and deep learning (conditional variational autoencoder architecture) methods to explore classification of patients/controls based on network connectivity. We then studied the network topology of the data, and developed a multislice modularity method to look for consensus evidence of modular reorganisation in chronic back pain. Results. Machine learning and deep learning allowed reliable classification of patients in a third, independent open data set with an accuracy of 63%, with 68% in cross validation of all data. We identified robust evidence of network hub disruption in chronic pain, most consistently with respect to clustering coefficient and betweenness centrality. We found a consensus pattern of modular reorganisation involving extensive, bilateral regions of sensorimotor cortex, and characterised primarily by negative reorganisation - a tendency for sensorimotor cortex nodes to be less inclined to form pairwise modular links with other brain nodes. In contrast, intraparietal sulcus displayed a propensity towards positive modular reorganisation, suggesting that it might have a role in forming modules associated with the chronic pain state. Conclusion. The results provide evidence of consistent and characteristic brain network changes in chronic pain, characterised primarily by extensive reorganisation of the network architecture of the sensorimotor cortex.

The control of tonic pain by active relief learning

Tonic pain after injury characterises a behavioural state that prioritises recovery. Although generally suppressing cognition and attention, tonic pain needs to allow effective relief learning to reduce the cause of the pain. Here, we describe a central learning circuit that supports learning of relief and concurrently suppresses the level of ongoing pain. We used computational modelling of behavioural, physiological and neuroimaging data in two experiments in which subjects learned to terminate tonic pain in static and dynamic escape-learning paradigms. In both studies, we show that active relief-seeking involves a reinforcement learning process manifest by error signals observed in the dorsal putamen. Critically, this system uses an uncertainty ('associability') signal detected in pregenual anterior cingulate cortex that both controls the relief learning rate, and endogenously and parametrically modulates the level of tonic pain. The results define a self-organising learning circuit that reduces ongoing pain when learning about potential relief.

Response heterogeneity: Challenges for personalised medicine and big data approaches in psychiatry and chronic pain

Response rates to available treatments for psychological and chronic pain disorders are poor, and there is a considerable burden of suffering and disability for patients, who often cycle through several rounds of ineffective treatment. As individuals presenting to the clinic with symptoms of these disorders are likely to be heterogeneous, there is considerable interest in the possibility that different constellations of signs could be used to identify subgroups of patients that might preferentially benefit from particular kinds of treatment. To this end, there has been a recent focus on the application of machine learning methods to attempt to identify sets of predictor variables (demographic, genetic, etc.) that could be used to target individuals towards treatments that are more likely to work for them in the first instance. Importantly, the training of such models generally relies on datasets where groups of individual predictor variables are labelled with a binary outcome category - usually 'responder' or 'non-responder' (to a particular treatment). However, as previously highlighted in other areas of medicine, there is a basic statistical problem in classifying individuals as 'responding' to a particular treatment on the basis of data from conventional randomized controlled trials. Specifically, insufficient information on the partition of variance components in individual symptom changes mean that it is inappropriate to consider data from the active treatment arm alone in this way. This may be particularly problematic in the case of psychiatric and chronic pain symptom data, where both within-subject variability and measurement error are likely to be high. Here, we outline some possible solutions to this problem in terms of dataset design and machine learning methodology, and conclude that it is important to carefully consider the kind of inferences that particular training data are able to afford, especially in arenas where the potential clinical benefit is so large.

Response heterogeneity: Challenges for personalised medicine and big data approaches in psychiatry and chronic pain

Response rates to available treatments for psychological and chronic pain disorders are poor, and there is a substantial burden of suffering and disability for patients, who often cycle through several rounds of ineffective treatment. As individuals presenting to the clinic with symptoms of these disorders are likely to be heterogeneous, there is considerable interest in the possibility that different constellations of signs could be used to identify subgroups of patients that might preferentially benefit from particular kinds of treatment. To this end, there has been a recent focus on the application of machine learning methods to attempt to identify sets of predictor variables (demographic, genetic, etc.) that could be used to target individuals towards treatments that are more likely to work for them in the first instance. Importantly, the training of such models generally relies on datasets where groups of individual predictor variables are labelled with a binary outcome category - usually 'responder' or 'non-responder' (to a particular treatment). However, as previously highlighted in other areas of medicine, there is a basic statistical problem in classifying individuals as 'responding' to a particular treatment on the basis of data from conventional randomized controlled trials. Specifically, insufficient information on the partition of variance components in individual symptom changes mean that it is inappropriate to consider data from the active treatment arm alone in this way. This may be particularly problematic in the case of psychiatric and chronic pain symptom data, where both within-subject variability and measurement error are likely to be high. Here, we outline some possible solutions to this problem in terms of dataset design and machine learning methodology, and conclude that it is important to carefully consider the kind of inferences that particular training data are able to afford, especially in arenas where the potential clinical benefit is so large.

Value generalization in human avoidance learning

Generalization during aversive decision-making allows us to avoid a broad range of potential threats following experience with a limited set of exemplars. However, over-generalization, resulting in excessive and inappropriate avoidance, has been implicated in a variety of psychological disorders. Here, we use reinforcement learning modelling to dissect out different contributions to the generalization of instrumental avoidance in two groups of human volunteers (N = 26, N = 482). We found that generalization of avoidance could be parsed into perceptual and value-based processes, and further, that value-based generalization could be subdivided into that relating to aversive and neutral feedback - with corresponding circuits including primary sensory cortex, anterior insula, amygdala and ventromedial prefrontal cortex. Further, generalization from aversive, but not neutral, feedback was associated with self-reported anxiety and intrusive thoughts. These results reveal a set of distinct mechanisms that mediate generalization in avoidance learning, and show how specific individual differences within them can yield anxiety.

Induced sensorimotor brain plasticity controls pain in phantom limb patients

The cause of pain in a phantom limb after partial or complete deafferentation is an important problem. A popular but increasingly controversial theory is that it results from maladaptive reorganization of the sensorimotor cortex, suggesting that experimental induction of further reorganization should affect the pain, especially if it results in functional restoration. Here we use a brain–machine interface (BMI) based on real-time magnetoencephalography signals to reconstruct affected hand movements with a robotic hand. BMI training induces significant plasticity in the sensorimotor cortex, manifested as improved discriminability of movement information and enhanced prosthetic control. Contrary to our expectation that functional restoration would reduce pain, the BMI training with the phantom hand intensifies the pain. In contrast, BMI training designed to dissociate the prosthetic and phantom hands actually reduces pain. These results reveal a functional relevance between sensorimotor cortical plasticity and pain, and may provide a novel treatment with BMI neurofeedback.

Pain in a phantom limb after limb deafferentation may be due to maladaptive sensorimotor representation. Here the authors find that sensorimotor plasticity induced by BMI training with the phantom hand, contrary to expectation, increased pain while dissociating prosthetic movements from the phantom arm relieved the pain.

Deep brain stimulation of the subthalamic nucleus modulates sensitivity to decision outcome value in Parkinson's disease

Deep brain stimulation (DBS) of the subthalamic nucleus in Parkinson’s disease is known to cause a subtle but important adverse impact on behaviour, with impulsivity its most widely reported manifestation. However, precisely which computational components of the decision process are modulated is not fully understood. Here we probe a number of distinct subprocesses, including temporal discount, outcome utility, instrumental learning rate, instrumental outcome sensitivity, reward-loss trade-offs, and perseveration. We tested 22 Parkinson’s Disease patients both on and off subthalamic nucleus deep brain stimulation (STN-DBS), while they performed an instrumental learning task involving financial rewards and losses, and an inter-temporal choice task for financial rewards. We found that instrumental learning performance was significantly worse following stimulation, due to modulation of instrumental outcome sensitivity. Specifically, patients became less sensitive to decision values for both rewards and losses, but without any change to the learning rate or reward-loss trade-offs. However, we found no evidence that DBS modulated different components of temporal impulsivity. In conclusion, our results implicate the subthalamic nucleus in a modulation of outcome value in experience-based learning and decision-making in Parkinson’s disease, suggesting a more pervasive role of the subthalamic nucleus in the control of human decision-making than previously thought.

Measurements in wound healing with observations on the effects of topical agents on full thickness dermal incised wounds

INTRODUCTION

A multitude of topical wound treatments are used today. Although it is well established that the micro-environment of healing wounds can be altered to improve healing, it is difficult to measure the subtle differences in outcome where therapies are compared.

METHOD

We compared wound healing properties between four different topical agents in surgically incised wounds in a pig model. The four topical agents, 5% Povidone-Iodine cream, 1% Silver-Sulphadiazine, 2% Mupirocin, and 1% Silver-Sulphadiazine plus 1mg/100g recombinant-human epithelial growth factor (EGF) were randomly assigned to four test animals each. Test agents were compared to each other and to untreated controls. We investigated existing and new methodologies of measurement of wound healing: clinical and histological visual scoring systems, immuno-histochemistry, and computerized image analysis of the wounds on days 3, 7, and 28.

RESULTS

All agents were found to have improved healing rates with better cellular architecture. Healing was faster, histological appearance resembled normal architecture sooner, clinical appearance improved, mitotic activity was stimulated and more collagen was deposited in comparison to the wounds with no agents. EGF-treated wounds showed an increased rate of epithelisation, but the rate of healing did not correlate well with evaluation of cosmetic outcome.

CONCLUSION

Topical agents improve all aspects of wound healing. The addition of a human recombinant EGF to Silver-Sulphadiazine increases epithelial growth and amounts of collagen in the regenerating wounds at day 7.

Dissociable Learning Processes Underlie Human Pain Conditioning

Pavlovian conditioning underlies many aspects of pain behavior, including fear and threat detection [1], escape and avoidance learning [2], and endogenous analgesia [3]. Although a central role for the amygdala is well established [4], both human and animal studies implicate other brain regions in learning, notably ventral striatum and cerebellum [5]. It remains unclear whether these regions make different contributions to a single aversive learning process or represent independent learning mechanisms that interact to generate the expression of pain-related behavior. We designed a human parallel aversive conditioning paradigm in which different Pavlovian visual cues probabilistically predicted thermal pain primarily to either the left or right arm and studied the acquisition of conditioned Pavlovian responses using combined physiological recordings and fMRI. Using computational modeling based on reinforcement learning theory, we found that conditioning involves two distinct types of learning process. First, a non-specific “preparatory” system learns aversive facial expressions and autonomic responses such as skin conductance. The associated learning signals—the learned associability and prediction error—were correlated with fMRI brain responses in amygdala-striatal regions, corresponding to the classic aversive (fear) learning circuit. Second, a specific lateralized system learns “consummatory” limb-withdrawal responses, detectable with electromyography of the arm to which pain is predicted. Its related learned associability was correlated with responses in ipsilateral cerebellar cortex, suggesting a novel computational role for the cerebellum in pain. In conclusion, our results show that the overall phenotype of conditioned pain behavior depends on two dissociable reinforcement learning circuits.

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Different brain learning systems are associated with different defensive responses

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Cerebellar responses correlate with “associability” for ipsilateral predicted pain

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The overall phenotype of conditioned pain is the sum of two part-independent processes

A NEURAL BIOMARKER FOR CHRONIC PAIN BASED ON DECODED BRAIN NETWORKS

The lack of a biomarker for chronic pain remains an important impediment to clinical and translational pain research. The problem stems from the multiple parallel but subtle abnormalties thought to represent the chronic pain state, yielding the emerging view of chronic pain as a ‘network disorder’. This suggests analysis approaches that aim to identify distributed patterns of data (multivariate, machine learning methods) might offer the best opportunity to discover biomarkers. Here, we performed a multi-center functional brain imaging study to record state functional brain networks resting in 41 patients with chronic back pain and 33 healthy control subjects. We calculated with functional covariance matrix from 160 regions of interest, and used Sparse Multinomial Logistic Regression to classify subjects as patient or control using a leave-one-out cross validation. Diagnostic accuracy was 91.9%, with sensitivity and specificity 90.2% and 93.9% respectively. We then used graph theoretic measures to characterise the pattern of network differences between the groups, and showed that the chronic pain state was associated with disrupted network ‘assortativity’. These data provide evidence to support an accurate functional biomarker of chronic pain, and open the door to the development of translatable biomarkers using similar methodologies in animals.

Accounting for Behavior in Treatment Effects: New Applications for Blind Trials

The double-blind randomized controlled trial (DBRCT) is the gold standard of medical research. We show that DBRCTs fail to fully account for the efficacy of treatment if there are interactions between treatment and behavior, for example, if a treatment is more effective when patients change their exercise or diet. Since behavioral or placebo effects depend on patients' beliefs that they are receiving treatment, clinical trials with a single probability of treatment are poorly suited to estimate the additional treatment benefit that arises from such interactions. Here, we propose methods to identify interaction effects, and use those methods in a meta-analysis of data from blinded anti-depressant trials in which participant-level data was available. Out of six eligible studies, which included three for the selective serotonin re-uptake inhibitor paroxetine, and three for the tricyclic imipramine, three studies had a high (>65%) probability of treatment. We found strong evidence that treatment probability affected the behavior of trial participants, specifically the decision to drop out of a trial. In the case of paroxetine, but not imipramine, there was an interaction between treatment and behavioral changes that enhanced the effectiveness of the drug. These data show that standard blind trials can fail to account for the full value added when there are interactions between a treatment and behavior. We therefore suggest that a new trial design, two-by-two blind trials, will better account for treatment efficacy when interaction effects may be important.

Decisions about decisions

A major puzzle of decision making is how the brain decides which decision system to use at any one time. In this issue of Neuron, Lee et al. (2014) provide a theoretical, behavioral, and neurobiological account of a prefrontal reliability-based arbitration system.

Does temporal discounting explain unhealthy behavior? A systematic review and reinforcement learning perspective

The tendency to make unhealthy choices is hypothesized to be related to an individual's temporal discount rate, the theoretical rate at which they devalue delayed rewards. Furthermore, a particular form of temporal discounting, hyperbolic discounting, has been proposed to explain why unhealthy behavior can occur despite healthy intentions. We examine these two hypotheses in turn. We first systematically review studies which investigate whether discount rates can predict unhealthy behavior. These studies reveal that high discount rates for money (and in some instances food or drug rewards) are associated with several unhealthy behaviors and markers of health status, establishing discounting as a promising predictive measure. We secondly examine whether intention-incongruent unhealthy actions are consistent with hyperbolic discounting. We conclude that intention-incongruent actions are often triggered by environmental cues or changes in motivational state, whose effects are not parameterized by hyperbolic discounting. We propose a framework for understanding these state-based effects in terms of the interplay of two distinct reinforcement learning mechanisms: a “model-based” (or goal-directed) system and a “model-free” (or habitual) system. Under this framework, while discounting of delayed health may contribute to the initiation of unhealthy behavior, with repetition, many unhealthy behaviors become habitual; if health goals then change, habitual behavior can still arise in response to environmental cues. We propose that the burgeoning development of computational models of these processes will permit further identification of health decision-making phenotypes.

The neural signature of escalating frustration in humans

Mammalian studies show that frustration is experienced when goal-directed activity is blocked. Despite frustration's strongly negative role in health, aggression and social relationships, the neural mechanisms are not well understood. To address this we developed a task in which participants were blocked from obtaining a reward, an established method of producing frustration. Levels of experienced frustration were parametrically varied by manipulating the participants' motivation to obtain the reward prior to blocking. This was achieved by varying the participants' proximity to a reward and the amount of effort expended in attempting to acquire it. In experiment 1, we confirmed that proximity and expended effort independently enhanced participants' self-reported desire to obtain the reward, and their self-reported frustration and response vigor (key-press force) following blocking. In experiment 2, we used functional magnetic resonance imaging (fMRI) to show that both proximity and expended effort modulated brain responses to blocked reward in regions implicated in animal models of reactive aggression, including the amygdala, midbrain periaqueductal grey (PAG), insula and prefrontal cortex. Our findings suggest that frustration may serve an energizing function, translating unfulfilled motivation into aggressive-like surges via a cortical, amygdala and PAG network.

Prices need no preferences: social trends determine decisions in experimental markets for pain relief

OBJECTIVE

A standard view in health economics is that, although there is no market that determines the "prices" for health states, people can nonetheless associate health states with monetary values (or other scales, such as quality adjusted life year [QALYs] and disability adjusted life year [DALYs]). Such valuations can be used to shape health policy, and a major research challenge is to elicit such values from people; creating experimental "markets" for health states is a theoretically attractive way to address this. We explore the possibility that this framework may be fundamentally flawed-because there may not be any stable values to be revealed. Instead, perhaps people construct ad hoc values, influenced by contextual factors, such as the observed decisions of others.

METHOD

The participants bid to buy relief from equally painful electrical shocks to the leg and arm in an experimental health market based on an interactive second-price auction. Thirty subjects were randomly assigned to two experimental conditions where the bids by "others" were manipulated to follow increasing or decreasing price trends for one, but not the other, pain. After the auction, a preference test asked the participants to choose which pain they prefer to experience for a longer duration.

RESULTS

Players remained indifferent between the two pain-types throughout the auction. However, their bids were differentially attracted toward what others bid for each pain, with overbidding during decreasing prices and underbidding during increasing prices.

CONCLUSION

Health preferences are dissociated from market prices, which are strongly referenced to others' choices. This suggests that the price of health care in a free-market has the capacity to become critically detached from people's underlying preferences.

The effect of motivation on movement: a study of bradykinesia in Parkinson's disease

BACKGROUND

Bradykinesia is a cardinal feature of Parkinson's disease (PD). Despite its disabling impact, the precise cause of this symptom remains elusive. Recent thinking suggests that bradykinesia may be more than simply a manifestation of motor slowness, and may in part reflect a specific deficit in the operation of motivational vigour in the striatum. In this paper we test the hypothesis that movement time in PD can be modulated by the specific nature of the motivational salience of possible action-outcomes.

METHODOLOGY/PRINCIPAL FINDINGS

We developed a novel movement time paradigm involving winnable rewards and avoidable painful electrical stimuli. The faster the subjects performed an action the more likely they were to win money (in appetitive blocks) or to avoid a painful shock (in aversive blocks). We compared PD patients when OFF dopaminergic medication with controls. Our key finding is that PD patients OFF dopaminergic medication move faster to avoid aversive outcomes (painful electric shocks) than to reap rewarding outcomes (winning money) and, unlike controls, do not speed up in the current trial having failed to win money in the previous one. We also demonstrate that sensitivity to distracting stimuli is valence specific.

CONCLUSIONS/SIGNIFICANCE

We suggest this pattern of results can be explained in terms of low dopamine levels in the Parkinsonian state leading to an insensitivity to appetitive outcomes, and thus an inability to modulate movement speed in the face of rewards. By comparison, sensitivity to aversive stimuli is relatively spared. Our findings point to a rarely described property of bradykinesia in PD, namely its selective regulation by everyday outcomes.