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.

Gamma-Band Oscillations Preferential for Nociception can be Recorded in the Human Insula.

Transient nociceptive stimuli elicit robust phase-locked local field potentials (LFPs) in the human insula. However, these responses are not preferential for nociception, as they are also elicited by transient non-nociceptive vibrotactile, auditory, and visual stimuli. Here, we investigated whether another feature of insular activity, namely gamma-band oscillations (GBOs), is preferentially observed in response to nociceptive stimuli. Although nociception-evoked GBOs have never been explored in the insula, previous scalp electroencephalography and magnetoencephalography studies suggest that nociceptive stimuli elicit GBOs in other areas such as the primary somatosensory and prefrontal cortices, and that this activity could be closely related to pain perception. Furthermore, tracing studies showed that the insula is a primary target of spinothalamic input. Using depth electrodes implanted in 9 patients investigated for epilepsy, we acquired insular responses to brief thermonociceptive stimuli and similarly arousing non-nociceptive vibrotactile, auditory, and visual stimuli (59 insular sites). As compared with non-nociceptive stimuli, nociceptive stimuli elicited a markedly stronger enhancement of GBOs (150-300 ms poststimulus) at all insular sites, suggesting that this feature of insular activity is preferential for thermonociception. Although this activity was also present in temporal and frontal regions, its magnitude was significantly greater in the insula as compared with these other regions.

Fast Multiscale Neighbor Embedding.

Dimension reduction (DR) computes faithful low-dimensional (LD) representations of high-dimensional (HD) data. Outstanding performances are achieved by recent neighbor embedding (NE) algorithms such as t -SNE, which mitigate the curse of dimensionality. The single-scale or multiscale nature of NE schemes drives the HD neighborhood preservation in the LD space (LDS). While single-scale methods focus on single-sized neighborhoods through the concept of perplexity, multiscale ones preserve neighborhoods in a broader range of sizes and account for the global HD organization to define the LDS. For both single-scale and multiscale methods, however, their time complexity in the number of samples is unaffordable for big data sets. Single-scale methods can be accelerated by relying on the inherent sparsity of the HD similarities they involve. On the other hand, the dense structure of the multiscale HD similarities prevents developing fast multiscale schemes in a similar way. This article addresses this difficulty by designing randomized accelerations of the multiscale methods. To account for all levels of interactions, the HD data are first subsampled at different scales, enabling to identify small and relevant neighbor sets for each data point thanks to vantage-point trees. Afterward, these sets are employed with a Barnes-Hut algorithm to cheaply evaluate the considered cost function and its gradient, enabling large-scale use of multiscale NE schemes. Extensive experiments demonstrate that the proposed accelerations are, statistically significantly, both faster than the original multiscale methods by orders of magnitude, and better preserving the HD neighborhoods than state-of-the-art single-scale schemes, leading to high-quality LD embeddings. Public codes are freely available at https://github.com/cdebodt.

Dynamics of the perception and EEG signals triggered by tonic warm and cool stimulation.

Thermosensation is crucial for humans to probe the environment and detect threats arising from noxious heat or cold. Over the last years, EEG frequency-tagging using long-lasting periodic radiant heat stimulation has been proposed as a means to study the cortical processes underlying tonic heat perception. This approach is based on the notion that periodic modulation of a sustained stimulus can elicit synchronized periodic activity in the neuronal populations responding to the stimulus, known as a steady-state response (SSR). In this paper, we extend this approach using a contact thermode to generate both heat- and cold-evoked SSRs. Furthermore, we characterize the temporal dynamics of the elicited responses, relate these dynamics to perception, and assess the effects of displacing the stimulated skin surface to gain insight on the heat- and cold-sensitive afferents conveying these responses. Two experiments were conducted in healthy volunteers. In both experiments, noxious heat and innocuous cool stimuli were applied during 75 seconds to the forearm using a Peltier-based contact thermode, with intensities varying sinusoidally at 0.2 Hz. Displacement of the thermal stimulation on the skin surface was achieved by independently controlling the Peltier elements of the thermal probe. Continuous intensity ratings to sustained heat and cold stimulation were obtained in the first experiment with 14 subjects, and the EEG was recorded in the second experiment on 15 subjects. Both contact heat and cool stimulation elicited periodic EEG responses and percepts. Compared to heat stimulation, the responses to cool stimulation had a lower magnitude and shorter latency. All responses tended to habituate along time, and this response attenuation was most pronounced for cool compared to warm stimulation, and for stimulation delivered using a fixed surface compared to a variable surface.

Insular responses to transient painful and non-painful thermal and mechanical spinothalamic stimuli recorded using intracerebral EEG.

Brief thermo-nociceptive stimuli elicit low-frequency phase-locked local field potentials (LFPs) and high-frequency gamma-band oscillations (GBOs) in the human insula. Although neither of these responses constitute a direct correlate of pain perception, previous findings suggest that insular GBOs may be strongly related to the activation of the spinothalamic system and/or to the processing of thermal information. To disentangle these different features of the stimulation, we compared the insular responses to brief painful thermonociceptive stimuli, non-painful cool stimuli, mechano-nociceptive stimuli, and innocuous vibrotactile stimuli, recorded using intracerebral electroencephalograpic activity in 7 epileptic patients (9 depth electrodes, 58 insular contacts). All four types of stimuli elicited consistent low-frequency phase-locked LFPs throughout the insula, possibly reflecting supramodal activity. The latencies of thermo-nociceptive and cool low-frequency phase-locked LFPs were shorter in the posterior insula compared to the anterior insula, suggesting a similar processing of thermal input initiating in the posterior insula, regardless of whether the input produces pain and regardless of thermal modality. In contrast, only thermo-nociceptive stimuli elicited an enhancement of insular GBOs, suggesting that these activities are not simply related to the activation of the spinothalamic system or to the conveyance of thermal information.

Nonlinear Dimensionality Reduction With Missing Data Using Parametric Multiple Imputations.

Dimensionality reduction (DR) aims at faithfully and meaningfully representing high-dimensional (HD) data into a low-dimensional (LD) space. Recently developed neighbor embedding DR methods lead to outstanding performances, thanks to their ability to foil the curse of dimensionality. Unfortunately, they cannot be directly employed on incomplete data sets, which become ubiquitous in machine learning. Discarding samples with missing features prevents their LD coordinates computation and deteriorates the complete samples treatment. Common missing data imputation schemes are not appropriate in the nonlinear DR context either. Indeed, even if they model the data distribution in the feature space, they can, at best, enable the application of a DR scheme on the expected data set. In practice, one would, instead, like to obtain the LD embedding with the closest cost function value on average with respect to the complete data case. As the state-of-the-art DR techniques are nonlinear, the latter embedding results from minimizing the expected cost function on the incomplete database, not from considering the expected data set. This paper addresses these limitations by developing a general methodology for nonlinear DR with missing data, being directly applicable with any DR scheme optimizing some criterion. In order to model the feature dependences, an HD extension of Gaussian mixture models is first fitted on the incomplete data set. It is afterward employed under the multiple imputation paradigms to obtain a single relevant LD embedding, thus minimizing the cost function expectation. Extensive experiments demonstrate the superiority of the suggested framework over alternative approaches.