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This commentary is intended to find possible explanations for the low impact of computational modeling on pain research. We discuss the main strategies that have been used in building computational models for the study of pain. The analysis suggests that traditional models lack biological plausibility at some levels, they do not provide clinically relevant results, and they cannot capture the stochastic character of neural dynamics. On this basis, we provide some suggestions that may be useful in building computational models of pain with a wider range of applications.

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Introduction: It has been reported that inhibitory control at the superficial dorsal horn (SDH) can act in a regionally distinct manner, which suggests that regionally specific subpopulations of SDH inhibitory neurons may prevent one specific neuropathic condition. Methods: In an attempt to address this issue, we provide an alternative approach by integrating neuroanatomical information provided by different studies to construct a network-model of the SDH. We use Neuroids to simulate each neuron included in that model by adapting available experimental evidence. Results: Simulations suggest that the maintenance of the proper level of pain sensitivity may be attributed to lamina II inhibitory neurons and, therefore, hyperalgesia may be elicited by suppression of the inhibitory tone at that lamina. In contrast, lamina III inhibitory neurons are more likely to be responsible for keeping the nociceptive pathway from the mechanoreceptive pathway, so loss of inhibitory control in that region may result in allodynia. The SDH network-model is also able to replicate non-linearities associated to pain processing, such as Aβ-fiber mediated analgesia and frequency-dependent increase of the neural response. Discussion: By incorporating biophysical accuracy and newer experimental evidence, the SDH network-model may become a valuable tool for assessing the contribution of specific SDH connectivity patterns to noxious transmission in both physiological and pathological conditions.

Background: One of the biggest obstacles to reliable pulse rate variability (PRV) analysis is the erroneous detection of photoplethysmographic (PPG) pulses. Among all the disturbances that may hinder pulse detection, the ripples appearing at the smooth segments of the PPG signal can become a serious problem when the amplitude of the signal decreases considerably. Objective: To present a low-complexity PPG pulse detection method for reliable PRV estimation under conditions in which a sudden decrease in the amplitude of the PPG signal can be expected. Approach: 2-min ECG and PPG data (sampling rate at 500 Hz) were obtained from thirty healthy subjects, who were asked to take a deep inspiration to provoke a sudden amplitude decrease (SAD) of the PPG signal. After introducing a new parameter denoted as C, through which it is possible to jump over the ripples hindering the accurate detection of the systolic peaks, 500 Hz-sampled PPG recordings were down-sampled (400, 300, 200 and 100 Hz) to investigate the effect of the sampling rate on pulse detection. For ECG recordings, automatic R-peak detection was performed by the Pan and Tompkins (PT) algorithm, whereas PPG pulse detection was performed by the well-known maximum of the first derivative (M1D) and the proposed method, once the C-value for best detection results on 500 Hz-sampled PPG recordings was found. The agreement between heart rate variability (HRV) and PRVs estimated from each pulse detection method was assessed and the correlation between HRV and PRV-derived indexes was computed for comparison. Main results: The proposed method can perform well on PPG-SAD segments, provided that the proper value of the parameter C is used. Moreover, a good agreement between HRV and PRV series, as well as lower relative errors and higher correlation coefficients between HRV and PRV indexes, were achieved by the proposed pulse detection method during SADs. Significance: Results show that the proposed method can dynamically adapt to circumstances in which a decrease in the amplitude of the PPG signal can be expected, providing continuous systolic peak detection and reliable PRV estimation under those conditions. However, more extensive testing under a wide range of conditions is needed to perform a more rigorous validation.