The Computational Neuroanatomy of Predictive Dynamics of Pain Perception

Abstract

Pain perception is an active process that regulates nociceptive inputs by descending opioidergic signals, in which the brain encodes pain-related predictive and corrective terms, after having made Bayesian-like inferences about noxious amplitudes. Offset analgesia (OA), a large reduction of tonic pain after a small nociceptive termination, is typical empirical evidence of on-line pain modulation through prediction and its correction. However, the basic computational structure underlying OA is not understood. Here, we adopted a constructive approach, formulated the inference of noxious amplitudes with a Kalman filter model, i.e., a recursive Bayesian computation, and then deduced the computational structure for OA, in which an interaction between two latent state variables was implemented. Simulation results suggested that the unidirectional interaction of the two states with two dissociable roles (an integral over time and a derivative of stimulus changes) is crucial for OA. Our results, combined with previous anatomical studies, suggest a computational basis of neural connectivity for pain. The ACC and aINS interact to compute a descending prediction to the brainstem, i.e. PAG, while ascending inputs are filtered in the thalamus and delivered to the cortices as prediction errors. Thus, we suggest dissociable, computational roles of the ACC and aINS in pain processing. Author SummaryUnderstanding the computational theory of pain perception is crucial for clarifying why some painful syndromes become chronic. Here, we propose a computational neuroanatomical model of endogenous pain modulation and we simulate a model for offset analgesia. We first demonstrate through model comparisons that the brain encodes at least two distinct states to estimate ongoing nociception: a derivative of input changes and its integral. We suggest that its neural substrate comprises hierarchical circuits composed of cortices, the thalamus, and brainstem. Second, we show that the computational basis of disrupted pain modulation in patients is pseudo-neglect of actual sensory inputs, with bias toward the internal prediction. Our results are the first to provide a neurocomputational mechanism of pain perception dynamics and a factor that determines its functionality.