Endogenous controllability of closed-loop brain-machine interfaces for pain

Zhang, Suyi; Yoshida, Wako; Matsubara, Hiroaki; Yanagisawa, Takufumi; Shibata, Kazuhisa; Kawato, Mitsuo; Seymour, Ben

doi:10.1101/369736

Cited by 2 publications

(3 citation statements)

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“…In the case of pain, therefore, the magnitude of a phasic pain stimulus should be enhanced if uncertainty, opportunity, and controllability are high, because the marginal benefit of learning is higher. More precisely, the model predicts these factors should interact, because the benefit of learning is only manifest if the opportunity and controllability are both significant (Zhang et al, 2018b) (see Box 1). In the case of learning relief from tonic pain, the opposite effect should occur (i.e., background pain should be reduced if the benefit to learning about relief is high), because the object of learning is relief, not pain (and persistent pain exerts a tonic control effect on behavior, as we discuss below).…”

Section: Modulation By Informational Valuementioning

confidence: 99%

“…However, several cortical regions seem to play a key role, including regions of anterior cingulate cortex, dorsolateral prefrontal cortex, and insula (Wiech, 2016;Tracey, 2010). More specifically, the pregenual anterior cingulate cortex (pgACC) has emerged as the most consistently implicated cortical region in human endogenous control paradigms, including in placebo and expectancy hypoalgesia (Wager et al, 2004;Bingel et al, 2006;Eippert et al, 2009), uncertainty-based analgesia (Zhang et al, 2018a(Zhang et al, , 2018b, controllability (Salomons et al, 2007(Salomons et al, , 2015, habituation (Bingel et al, 2007), stressinduced analgesia (Yilmaz et al, 2010), and even analgesia induced by motor cortex stimulation (Peyron et al, 2007). The pgACC is highly opioid rich and sits within an anatomical network with connections to PAG and other subcortical regions involved in pain and learning, including amygdala, VMPFC, hippocampus, Box 1.…”

Section: Modulation By Informational Valuementioning

confidence: 99%

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Pain: A Precision Signal for Reinforcement Learning and Control

Seymour

2019

Neuron

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115

104

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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.Despite the advent of brain imaging, a clear picture of how pain is processed in the brain has been much harder to unravel than anticipated, being beset by three problems. First, pain is associated with robust responses in multiple and diverse brain regions, most of which are not specific to pain (at least on a macroscopic scale), and so it has been hard to ''pin down'' the pain system to any specific brain region. Second, pain is an inherently private percept, but an individual's self-reports of pain can vary widely from moment to moment, and it has remained unclear whether this fluctuation represents irreducible noise and subjectivity or a precise tuning of pain based on hidden factors. Third, pain is exquisitely sensitive to a broad range of emotional, environmental, and cognitive factors-a phenomenon called endogenous modulation. Although this has led to an appreciation that pain is more than a simple readout of nociceptive input, it has not led to any satisfactory unified explanation as to what pain really is. This has left the view that pain is simply a highly variable and malleable representation of assumed actual or potential tissue damage.In this review, we propose a model of pain that centralizes its role as a learning and control signal and argue that this can solve these problems. We begin with a perspective of how theories of pain have evolved over recent decades, and how insights have emerged that have moved thinking beyond purely sensory accounts of pain. We then argue that current accounts still don't fully capture how pain controls behavior to minimize harm, which is its primary function. Importantly, although this is often achieved by immediate nocifensive responses, a substantial part of this comes from learning-allowing an animal to mitigate or avoid predictable harm long into the future. The foundations of a learning account of pain are rooted in psychological models of animal learning, and we describe how these can be developed in computational terms to provide a mechanistic model of the architecture of the pain system. Critically, we argue that this requires pain to be shaped by a set of factors to optimize its role as a learning and control signal and review evidence that suggests that a great...

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Section: Modulation By Informational Valuementioning

confidence: 99%

Section: Modulation By Informational Valuementioning

confidence: 99%

Pain: A Precision Signal for Reinforcement Learning and Control

Seymour

2019

Neuron

Self Cite

115

104

View full text Add to dashboard Cite

show abstract

“…The relation between the central processing of nociceptive information and the MI is emerging in the BCI context [26]; but the use of NWR as a feedback is still unknown.…”

Section: Introductionmentioning

confidence: 99%