Computational and neural mechanisms of statistical pain learning

Mancini, Flavia; Zhang, Suyi; Seymour, Ben

doi:10.1038/s41467-022-34283-9

Cited by 22 publications

(20 citation statements)

References 61 publications

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“…Much of what is known about pain processing has resulted from studies that induce pain in healthy volunteers using brief, millisecond-long stimuli with infrared lasers (Markman et al, 2013; Kim et al, 2015; Liu et al, 2015). Such work has revealed pain networks comprise of regions such as the somatosensory, insular, and prefrontal cortices (Davis et al, 2002; Wager et al, 2013; Asad et al, 2016; Xu et al, 2020; Mancini et al, 2022). Noninvasive imaging studies inducing tonic pain with thermodes suggest that ongoing pain activates similar brain regions as brief experimental stimuli (Schreckenberger et al, 2005; Owen et al, 2010; Wasan et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“Characterization of spatiotemporal dynamics of binary and graded tonic pain in humans using intracranial recordings”

Caston

Smith

Davis

et al. 2023

Preprint

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Pain is a complex experience involving sensory, emotional, and cognitive aspects, and multiple networks manage its processing in the brain. Examining how pain transforms into a behavioral response can shed light on the networks' relationships and facilitate interventions to treat chronic pain. However, studies using high spatial and temporal resolution methods to investigate the neural encoding of pain and its psychophysical correlates have been limited. We recorded from intracranial stereo-EEG (sEEG) electrodes implanted in sixteen different brain regions of twenty patients who underwent psychophysical pain testing consisting of a tonic thermal stimulus to the hand. Broadband high-frequency local field potential amplitude (HFA; 70-150 Hz) was isolated to investigate the relationship between the ongoing neural activity and the resulting psychophysical pain evaluations. Two different generalized linear mixed-effects models (GLME) were employed to assess the neural representations underlying binary and graded pain psychophysics. The first model examined the relationship between HFA and whether the patient responded "yes" or "no" to whether the trial was painful. The second model investigated the relationship between HFA and how painful the stimulus was rated on a visual analog scale. GLMEs revealed that HFA in the inferior temporal gyrus (ITG), superior frontal gyrus (SFG), and superior temporal gyrus (STG) predicted painful responses at stimulus onset. An increase in HFA in the orbitofrontal cortex (OFC), SFG, and striatum predicted pain responses at stimulus offset. Numerous regions including the anterior cingulate cortex, hippocampus, IFG, MTG, OFC, and striatum, predicted the pain rating at stimulus onset. However, only the amygdala and fusiform gyrus predicted increased pain ratings at stimulus offset. We characterized the spatiotemporal representations of binary and graded painful responses during tonic pain stimuli. Our study provides evidence from intracranial recordings that the neural encoding of psychophysical pain changes over time during a tonic thermal stimulus, with different brain regions being predictive of pain at the beginning and end of the stimulus.

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Section: Introductionmentioning

confidence: 99%

“Characterization of spatiotemporal dynamics of binary and graded tonic pain in humans using intracranial recordings”

Caston

Smith

Davis

et al. 2023

Preprint

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show abstract

“…For instance, [35][36][37] reported that chronic back pain ratings vary periodically, over several seconds-minutes and in absence of movements. This temporal aspect of pain is important because periodic temporal structures are easy to learn for the brain [1,8]. If the temporal evolution of pain is learned, it can be used by the brain to regulate its responses to forthcoming pain, effectively shaping how much pain it experiences.…”

Section: Discussionmentioning

confidence: 99%

“…The temporal structure of these signals is important, because the human brain has evolved the exceptional ability to extract regularities from streams of auto-correlated sensory signals, a process called statistical learning [1][2][3][4][5][6][7]. In the context of pain, statistical learning can allow the brain to predict future pain, which is crucial for orienting behaviour and maximising well-being [8,9]. Statistical learning might also be fundamental to the ability of the nervous system to endogenously regulate pain.…”

Section: Introductionmentioning

confidence: 99%

“…By using temporal sequences of noxious inputs, we have previously shown that the pain system supports the statistical learning of the basic rate of getting pain by engaging both somatosensory and supramodal cortical regions [8]. Specifically, both sensorimotor cortical regions and the ventral striatum encode probabilistic predictions about pain intensity, which are updated as a function of learning by engaging parietal and prefrontal regions.…”

Section: Introductionmentioning

confidence: 99%

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Statistical learning shapes pain perception and prediction independently of external cues

Onysk

Whitefield

Gregory

et al. 2023

Preprint

Self Cite

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The placebo and nocebo effects highlight the importance of expectations in modulating pain perception, but in every day life we don't need an external source of information to form expectations about pain. The brain can learn to predict pain in a more fundamental way, simply by experiencing fluctuating, non-random streams of noxious inputs, and extracting their temporal regularities. This process is called statistical learning. Here we address two key open questions: (1) does statistical learning modulate pain perception? and (2) is it different in people with chronic musculoskeletal pain? In a first experiment, we asked 27 participants to both rate and predict pain intensity levels in sequences of fluctuating heat pain. Using a computational approach, we show that probabilistic expectations and confidence were used to weight pain perception and prediction. Given that statistical learning involves supramodal processes, we developed an online, stock market game to assess the ability to explicitly predict volatile and stochastic time series, probing the most fundamental components of statistical learning. The game was played by 56 chronic back pain and 55 healthy participants. We show that back pain participants learn the statistics of the sequence more slowly than controls. In conclusion, this study shows that statistical learning shapes pain experience and can be disrupted in common chronic pain conditions, opening a new path of research into the brain mechanisms of pain regulation

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“…Because of the distributed nature of pain processing, a holistic, systems-level understanding of how different neural circuits transfer, coordinate, and integrate information still remains elusive. In addition, several computational theories have been proposed in pain studies (see a review in Chen and Wang, 2023), including reinforcement learning and control (Seymour, 2019;Seymour and Mancini, 2020;Mancini et al, 2022;Seymour et al, 2023), and predictive coding (Büchel et al, 2014;Wiech, 2016;Ploner et al, 2017;Jepma et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Hierarchical predictive coding in distributed pain circuits

Chen

2023

Front. Neural Circuits

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Predictive coding is a computational theory on describing how the brain perceives and acts, which has been widely adopted in sensory processing and motor control. Nociceptive and pain processing involves a large and distributed network of circuits. However, it is still unknown whether this distributed network is completely decentralized or requires networkwide coordination. Multiple lines of evidence from human and animal studies have suggested that the cingulate cortex and insula cortex (cingulate-insula network) are two major hubs in mediating information from sensory afferents and spinothalamic inputs, whereas subregions of cingulate and insula cortices have distinct projections and functional roles. In this mini-review, we propose an updated hierarchical predictive coding framework for pain perception and discuss its related computational, algorithmic, and implementation issues. We suggest active inference as a generalized predictive coding algorithm, and hierarchically organized traveling waves of independent neural oscillations as a plausible brain mechanism to integrate bottom-up and top-down information across distributed pain circuits.

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Computational and neural mechanisms of statistical pain learning

Cited by 22 publications

References 61 publications

“Characterization of spatiotemporal dynamics of binary and graded tonic pain in humans using intracranial recordings”

“Characterization of spatiotemporal dynamics of binary and graded tonic pain in humans using intracranial recordings”

Statistical learning shapes pain perception and prediction independently of external cues

Hierarchical predictive coding in distributed pain circuits

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