Neuronal Oscillations in Various Frequency Bands Differ between Pain and Touch

Although humans are generally capable of distinguishing single events of pain or touch, recent research suggested that both modalities activate a network of similar brain regions. By contrast, less attention has been paid to which processes uniquely contribute to each modality. The present study investigated the neuronal oscillations that enable a subject to process pain and touch as well as to evaluate the intensity of both modalities by means of Electroencephalography. Nineteen healthy subjects were asked to rate the intensity of each stimulus at single trial level. By computing Linear mixed effects models (LME) encoding of both modalities was explored by relating stimulus intensities to brain responses. While the intensity of single touch trials is encoded only by theta activity, pain perception is encoded by theta, alpha and gamma activity. Beta activity in the tactile domain shows an on/off like characteristic in response to touch which was not observed in the pain domain. Our results enhance recent findings pointing to the contribution of different neuronal oscillations to the processing of nociceptive and tactile stimuli

Prefrontal gamma oscillations encode tonic pain in humans

Under physiological conditions, momentary pain serves vital protective functions. Ongoing pain in chronic pain states, on the other hand, is a pathological condition that causes widespread suffering and whose treatment remains unsatisfactory. The brain mechanisms of ongoing pain are largely unknown. In this study, we applied tonic painful heat stimuli of varying degree to healthy human subjects, obtained continuous pain ratings, and recorded electroencephalograms to relate ongoing pain to brain activity. Our results reveal that the subjective perception of tonic pain is selectively encoded by gamma oscillations in the medial prefrontal cortex. We further observed that the encoding of subjective pain intensity experienced by the participants differs fundamentally from that of objective stimulus intensity and from that of brief pain stimuli. These observations point to a role for gamma oscillations in the medial prefrontal cortex in ongoing, tonic pain and thereby extend current concepts of the brain mechanisms of pain to the clinically relevant state of ongoing pain. Furthermore, our approach might help to identify a brain marker of ongoing pain, which may prove useful for the diagnosis and therapy of chronic pain

Ultra-high-field imaging reveals increased whole brain connectivity underpins cognitive strategies that attenuate pain

We investigated how the attenuation of pain with cognitive interventions affects brain connectivity using neuroimaging and a whole brain novel analysis approach. While receiving tonic cold pain, 20 healthy participants performed three different pain attenuation strategies during simultaneous collection of functional imaging data at seven tesla. Participants were asked to rate their pain after each trial. We related the trial-by-trial variability of the attenuation performance to the trial-by-trial functional connectivity strength change of brain data. Across all conditions, we found that a higher performance of pain attenuation was predominantly associated with higher functional connectivity. Of note, we observed an association between low pain and high connectivity for regions that belong to brain regions long associated with pain processing, the insular and cingulate cortices. For one of the cognitive strategies (safe place), the performance of pain attenuation was explained by diffusion tensor imaging metrics of increased white matter integrity

Interindividual variability and individual stability of pain- and touch-related neuronal gamma oscillations

The interpretation of gamma activity in response to noxious and innocuous somatosensory stimuli has sparked controversy. Here, we show that participants’ gamma oscillations measured through electroencephalography vary. Although some participants do not show a distinct gamma response, others exhibit stable and reliable response patterns in terms of time, frequency, and magnitude

Intrinsic network activity reflects the ongoing experience of chronic pain

Analyses of intrinsic network activity have been instrumental in revealing cortical processes that are altered in chronic pain patients. In a novel approach, we aimed to elucidate how intrinsic functional networks evolve in regard to the fluctuating intensity of the experience of chronic pain. In a longitudinal study with 156 fMRI sessions, 20 chronic back pain patients and 20 chronic migraine patients were asked to continuously rate the intensity of their endogenous pain. We investigated the relationship between the fluctuation of intrinsic network activity with the time course of subjective pain ratings. For chronic back pain, we found increased cortical network activity for the salience network and a local pontine network, as well as decreased network activity in the anterior and posterior default mode network for higher pain intensities. Higher pain intensities in chronic migraine were accompanied with lower activity in a prefrontal cortical network. By taking the perspective of the individual, we focused on the variability of the subjective perception of pain, which include phases of relatively low pain and phases of relatively high pain. The present design of the assessment of ongoing endogenous pain can be a powerful and promising tool to assess the signature of a patient's endogenous pain encoding

Patients with chronic pain exhibit individually unique cortical signatures of pain encoding

Chronic pain is characterised by an ongoing and fluctuating intensity over time. Here, we investigated how the trajectory of the patients\u27 endogenous pain is encoded in the brain. In repeated functional MRI (fMRI) sessions, 20 patients with chronic back pain and 20 patients with chronic migraine were asked to continuously rate the intensity of their endogenous pain. Linear mixed effects models were used to disentangle cortical processes related to pain intensity and to pain intensity changes. At group level, we found that the intensity of pain in patients with chronic back pain is encoded in the anterior insular cortex, the frontal operculum, and the pons; the change of pain in chronic back pain and chronic migraine patients is mainly encoded in the anterior insular cortex. At the individual level, we identified a more complex picture where each patient exhibited their own signature of endogenous pain encoding. The diversity of the individual cortical signatures of chronic pain encoding results bridge between clinical observations and neuroimaging; they add to the understanding of chronic pain as a complex and multifaceted disease

On the Exact Two-Sided Tolerance Intervals for Univariate Normal Distribution and Linear Regression

Statistical tolerance intervals are another tool for making statistical inference on an unknown population. The tolerance interval is an interval estimator based on the results of a calibration experiment, which can be asserted with stated confidence level 1 ? , for example 0.95, to contain at least a specified proportion 1 ? , for example 0.99, of the items in the population under consideration. Typically, the limits of the tolerance intervals functionally depend on the tolerance factors. In contrast to other statistical intervals commonly used for statistical inference, the tolerance intervals are used relatively rarely. One reason is that the theoretical concept and computational complexity of the tolerance intervals is significantly more difficult than that of the standard confidence and prediction intervals. In this paper we present a brief overview of the theoretical background and approaches for computing the tolerance factors based on samples from one or several univariate normal (Gaussian) populations, as well as the tolerance factors for the non-simultaneous and simultaneous two-sided tolerance intervals for univariate linear regression. Such tolerance intervals are well motivated by their applicability in the multiple-use calibration problem and in construction of the calibration confidence intervals. For illustration, we present examples of computing selected tolerance factors by the implemented algorithm in MATLAB

On the exact tolerance intervals for univariate normal distribution

Statistical tolerance interval is another type of interval estimator used for making statistical inference on an unknown population. Simply stated, it is an interval estimator, based on a sample from preliminary experiment, which can be asserted with confidence level 1−α (for example 0.95), to contain at least a speci- fied proportion, say 1−γ (for example 0.99), of the items in the population under consideration. The limits of a statistical tolerance interval are called statistical tolerance limits. The confidence level 1 − α is the probability that a statistical tolerance interval constructed in the prescribed manner (i.e. based on result of an experiment conducted under identical conditions) will contain at least a pro- portion 1 − γ of possibly infinite sequence of items coming from the considered (unknown) population (i.e. realizations of independent random variables from the given distribution). In contrast with other statistical intervals commonly used for statistical inference, like e.g. the confidence intervals for the parameters and/or the prediction intervals for future observation(s), the tolerance intervals are used relatively rarely. One reason is that the theoretical concept and compu- tational complexity is significantly more difficult, if compared with the commonly used confidence and prediction intervals. In this paper we briefly describe the theoretical background and computational approaches for computing the toler- ance factors and limits for statistical tolerance intervals based on samples from univariate normal (Gaussian) populations

Pain and the emotional brain: pain-related cortical processes are better reflected by affective evaluation than by cognitive evaluation

Abstract The experience of pain has been dissociated into two interwoven aspects: a sensory-discriminative aspect and an affective-motivational aspect. We aimed to explore which of the pain descriptors is more deeply rooted in the human brain. Participants were asked to evaluate applied cold pain. The majority of the trials showed distinct ratings: some were rated higher for unpleasantness and others for intensity. We compared the relationship between functional data recorded from 7 T MRI with unpleasantness and intensity ratings and revealed a stronger relationship between cortical data and unpleasantness ratings. The present study underlines the importance of the emotional-affective aspects of pain-related cortical processes in the brain. The findings corroborate previous studies showing a higher sensitivity to pain unpleasantness compared to ratings of pain intensity. For the processing of pain in healthy subjects, this effect may reflect the more direct and intuitive evaluation of emotional aspects of the pain system, which is to prevent harm and to preserve the physical integrity of the body

Strategy-dependent modulation of cortical pain circuits for the attenuation of pain

The effectiveness of cognitive strategies to attenuate pain has been reported in various behavioural studies, however the underlying neuronal mechanisms are only now beginning to be understood. Using a 7 T fMRI, we investigated three different pain attenuation strategies in 20 healthy subjects via: (a) non-imaginal distraction by counting backwards in steps of seven; (b) imaginal distraction by imagining a safe place; and (c) reinterpretation of the pain valence (reappraisal). Although we found considerable variability in the performances, all strategies exhibited a significant relief of pain compared to an unmodulated pain condition. Our finding argues against a subject\u27s potential predisposition for a certain attenuation approach, as some of the subjects performed well on all attenuation tasks yet others performed low on all attenuation tasks. We further investigated the variability of performance within-subjects and explored the cortical regions that contribute to successful single attempts of pain attenuation at trial level. For each of the three tasks, we found a different pattern of brain activity that reflects the performance of pain attenuation. The more successful trials are related to reduced activity of different parts of the insular cortex. Behavioural data suggest that distraction is the preferable cognitive strategy to modulate pain perception. For three different cognitive strategies we revealed brain regions that are suggested to reliably modulate the perception of pain. The findings could be of utmost benefit for future attempts to integrate neuroscientific techniques into the treatment of pain. Further studies are necessary to investigate whether the present results are transferable to patients as an essential part of the multimodal therapy for chronic pain. These patients may also benefit from additional neurofeedback techniques by combining the strategies with the cortical feedback in order to modulate pain-related brain activity