Virtual reality hypnosis diminishes experimental cold pain and alters autonomic responses

Immersive virtual reality (VR) is a promising tool to reduce pain in clinical setting. Digital scripts displayed by VR disposals can be enriched by several analgesic interventions, which are widely used to reduce pain. One of these techniques is hypnosis induced through the VR script (VRH) which is facilitated by immersive environment and particularly efficient even for low hypnotizable patients. The aim of this study is to assess the efficacy of a VRH script on experimentally induced cold pain perception (intensity and unpleasantness) and physiological expression. 41 healthy volunteers had been recruited in this within-subjects study. They received 9 stimulations of 20 s (3 non-nociceptive cold; 3 low nociceptive cold and 3 highly nociceptive cold) during a VRH session of 20 min (VRH condition) or without VRH (noVRH condition). Physiological monitoring during the cold pain stimulation protocol consisted of recording heart rate, heart rate variability and respiratory frequency. Maximum cold pain intensity perception, measured through the visual analog scale (VAS) on 10, was of 3.66 ± 1.84 (VAS score/10) in noVRH condition and 2.46 ± 1.54 in VRH (Wilcoxon, p < 0.0001). Considering pain unpleasantness perception, 3.68 ± 2.06 in noVRH and 2.21 ± 1.63 in VRH (Wilcoxon, p < 0.0001). Hypnotizability negatively correlated with the decrease in VAS intensity from noVRH to VRH (Spearman r = -0.45; p = 0.0038). In our sample, we found that 31/41 volunteers (75.6%) displayed a reduction of more than 10% of their VAS pain intensity and unpleasantness scores. Trait anxiety was the best predictor of the VRH responders, as well as heart rate variability. In addition, respiratory rate was diminished under VRH in every subgroup. VRH is an effective tool to reduced pain intensity and unpleasantness in a vast majority of healthy subjects. We further indicate in this study that heart rate variability parameter RMSSD (root mean square of successive differences) is a good predictor of this effect, as well as anxiety as a personality trait (but not state anxiety). Further studies are expected to determine more precisely to whom it will be the most useful to offer tailored, non-pharmacological pain management solutions to patients.

Effect of Virtual Reality Hypnosis on Pain Threshold and Neurophysiological and Autonomic Biomarkers in Healthy Volunteers: Prospective Randomized Crossover Study

Background

Virtual reality hypnosis (VRH) is a promising tool to reduce pain. However, the benefits of VRH on pain perception and on the physiological expression of pain require further investigation.

Objective

In this study, we characterized the effects of VRH on the heat pain threshold among adult healthy volunteers while monitoring several physiological and autonomic functions.

Methods

Sixty healthy volunteers were prospectively included to receive nociceptive stimulations. The first set of thermal stimuli consisted of 20 stimulations at 60°C (duration 500 milliseconds) to trigger contact heat evoked potentials (CHEPs). The second set of thermal stimuli consisted of ramps (1°C/second) to determine the heat pain threshold of the participants. Electrocardiogram, skin conductance responses, respiration rate, as well as the analgesia nociception index were also recorded throughout the experiment.

Results

Data from 58 participants were analyzed. There was a small but significant increase in pain threshold in VRH (50.19°C, SD 1.98°C) compared to that in the control condition (mean 49.45°C, SD 1.87; P<.001, Wilcoxon matched-pairs signed-rank test; Cohen d=0.38). No significant effect of VRH on CHEPs and heart rate variability parameters was observed (all P>0.5; n=22 and n=52, respectively). During VRH, participants exhibited a clear reduction in their autonomic sympathetic tone, as shown by the lower number of nonspecific skin conductance peak responses (P<.001, two-way analysis of variance; n=39) and by an increase in the analgesia nociception index (P<.001, paired t-test; n=40).

Conclusions

The results obtained in this study support the idea that VRH administration is effective at increasing heat pain thresholds and impacts autonomic functions among healthy volunteers. As a nonpharmacological intervention, VRH has beneficial action on acute experimental heat pain. This beneficial action will need to be evaluated for the treatment of other types of pain, including chronic pain.

Evaluation of cortical segmentation pipelines on clinical neonatal MRI data

Magnetic Resonance Imaging (MRI) can provide 3D morphological information on brain structures. Such information is particularly relevant for carrying out morphometric brain analysis, especially in the newborn and in the case of prematurity. However, 3D neonatal MRI acquired in clinical environments are low-resolution, anisotropic images, making segmentation a challenging task. In this context, preprocessing techniques aim to increase the image resolution. Interpolation techniques were classically used; super-resolution (SR) techniques have recently appeared as an emerging alternative. In this paper, we evaluate the performance of different SR methods against the classical interpolation in the application of neonatal cortex segmentation. Additionally, we assess the robustness of different segmentation methods for each estimation of high resolution MRI input. Results are evaluated both qualitatively and quantitatively with neonatal clinical MRI.

Self-trapping relaxation decay investigated by time-resolved photoelectron spectroscopy

The present work combines time-resolved photoelectron spectroscopy on isolated species with high-level data processing to address an issue which usually pertains to materials science: the electronic relaxation dynamics towards the formation of a self-trapped exciton (STE). Such excitons are common excited states in ionic crystals, silica and rare gas matrices. They are associated with a strong local deformation of the matrix. Argon clusters were taken as a model. They are excited initially to a Wannier exciton at 14 eV and their evolution towards the formation of an STE has showed an unusual type of vibronic relaxation where the electronic excitation of the cluster decreases linearly as a function of time with a 0.59 ± 0.06 eV ps-1 rate. The decay was followed for 3.0 ps, and the STE formation occurred in ∼5.1 ± 0.7 ps.

Segmentation of Mueller matrix images under non-uniform illumination

The recently proposed restoration-segmentation algorithms dedicated to polarization encoded images suffer two important limitations: the number of classes into which the image is segmented is not obtained automatically, and more importantly the quality of the segmentation is affected by the nonuniformity of the illumination of the scene. We propose here a new method addressing these issues. It is based on a global estimation-segmentation approach, explicitly modeling the nonuniform illumination. The physical admissibility of the retrieved Mueller matrices is ensured. Results stemming from synthetic and real data are provided and support the proposed approach.

Estimation of Mueller matrices using non-local means filtering

This article addresses the estimation of polarization signatures in the Mueller imaging framework by non-local means filtering. This is an extension of previous work dealing with Stokes signatures. The extension is not straightforward because of the gap in complexity between the Mueller framework and the Stokes framework. The estimation procedure relies on the Cholesky decomposition of the coherency matrix, thereby ensuring the physical admissibility of the estimate. We propose an original parameterization of the boundary of the set of Mueller matrices, which makes our approach possible. The proposed method is fully unsupervised. It allows noise removal and the preservation of edges. Applications to synthetic as well as real data are presented.

Topology preserving warping of 3-D binary images according to continuous one-to-one mappings

The estimation of one-to-one mappings is one of the most intensively studied topics in the research field of nonrigid registration. Although the computation of such mappings can be now accurately and efficiently performed, the solutions for using them in the context of binary image deformation is much less satisfactory. In particular, warping a binary image with such transformations may alter its discrete topological properties if common resampling strategies are considered. In order to deal with this issue, this paper proposes a method for warping such images according to continuous and bijective mappings while preserving their discrete topological properties (i.e., their homotopy type). Results obtained in the context of the atlas-based segmentation of complex anatomical structures highlight the advantages of the proposed approach.

Scanning ophthalmoscope retinal image registration using one-dimensional deformation fields

We present a new, robust and automated method for registering sequences of images acquired from scanning ophthalmoscopes. The method uses a multi-scale B-spline representation of the deformation field to map images to each other and an hierarchical optimization method. We applied the method to video sequences acquired from different parts of the retina. In all cases, the registration was successful, even in the presence of large distortions from microsaccades, and the resulting deformation fields describe the fixational motion of the eye. The registration reveals examples of dynamic photoreceptor behaviour in the sequences.

A multi-level parcellation approach for brain functional connectivity analysis

Most brain functional connectivity methods in fMRI require a brain parcellation into functionally homogeneous regions. In this work we propose a novel parcellation approach based on a spatial hierarchical clustering, that provides clusters within a multi-level framework. The method has the advantage of producing several brain parcellations rather than a single one from a fixed size-homogeneity criterion. Results obtained on real data demonstrate the relevance of the approach. Finally, a connectivity study shows the benefit of a prior multi-level parcellation of the brain.

An a contrario approach for change detection in 3D multimodal images: application to multiple sclerosis in MRI

Estimating significant changes between two images remains a challenging problem in medical image processing. This paper proposes a non-parametric region based method to detect significant changes in 3D multimodal Magnetic Resonance (MR) sequences. The proposed approach relies on an a contrario model which defines significant changes as events with very low probability. We adapt the a contrario framework to deal with multimodal images from which are extracted measures related to intensity and volume changes. Two fusion rules are carefully designed to handle a set of decision thresholds and a set of image measures. The final decision is taken using multiple testing procedures. The efficiency of the algorithm is demonstrated in the context of multiple sclerosis (MS) lesion analysis over time in multimodal MR sequences. We evaluate the proposed method on synthetic images using the Brainweb simulator. Finally, promising results on multimodal sequences on clinical data are presented.

Hidden Markov multiple event sequence models: A paradigm for the spatio-temporal analysis of fMRI data

This paper presents a novel, completely unsupervised fMRI brain mapping method that addresses the three problems of hemodynamic response function (HRF) variability, hemodynamic event timing, and fMRI response non-linearity. Spatial and temporal information are directly taken into account into the core of the activation detection process. In practice, activation detection at voxel v is formulated in terms of temporal alignment between sequences of hemodynamic response onsets (HROs) detected in the fMRI signal at v and in the spatial neighborhood of v, and the input sequence of stimuli or stimulus onsets. Event-related and epoch paradigms are considered. The multiple event sequence alignment problem is solved within the probabilistic framework of hidden Markov multiple event sequence models (HMMESMs), a new class of hidden Markov models. Results obtained on real and synthetic data significantly outperform those obtained with the popular statistical parametric mapping (SPM2) method without requiring any prior definition of the expected activation patterns, the HMMESM mapping approach being completely unsupervised.

Unsupervised learning and mapping of active brain functional MRI signals based on hidden semi-Markov event sequence models

In this paper, a novel functional magnetic resonance imaging (fMRI) brain mapping method is presented within the statistical modeling framework of hidden semi-Markov event sequence models (HSMESMs). Neural activation detection is formulated at the voxel level in terms of time coupling between the sequence of hemodynamic response onsets (HROs) observed in the fMRI signal, and an HSMESM of the hidden sequence of task-induced neural activations. The sequence of HRO events is derived from a continuous wavelet transform (CWT) of the fMRI signal. The brain activation HSMESM is built from the timing information of the input stimulation protocol. The rich mathematical framework of HSMESMs makes these models an effective and versatile approach for fMRI data analysis. Solving for the HSMESM Evaluation and Learning problems enables the model to automatically detect neural activation embedded in a given set of fMRI signals, without requiring any template basis function or prior shape assumption for the fMRI response. Solving for the HSMESM Decoding problem allows to enrich brain mapping with activation lag mapping, activation mode visualizing, and hemodynamic response function analysis. Activation detection results obtained on synthetic and real epoch-related fMRI data demonstrate the superiority of the HSMESM mapping method with respect to a real application case of the statistical parametric mapping (SPM) approach. In addition, the HSMESM mapping method appears clearly insensitive to timing variations of the hemodynamic response, and exhibits low sensitivity to fluctuations of its shape.

Hidden Markov event sequence models: toward unsupervised functional MRI brain mapping

RATIONALE AND OBJECTIVES

Most methods used in functional MRI (fMRI) brain mapping require restrictive assumptions about the shape and timing of the fMRI signal in activated voxels. Consequently, fMRI data may be partially and misleadingly characterized, leading to suboptimal or invalid inference. To limit these assumptions and to capture the broad range of possible activation patterns, a novel statistical fMRI brain mapping method is proposed. It relies on hidden semi-Markov event sequence models (HSMESMs), a special class of hidden Markov models (HMMs) dedicated to the modeling and analysis of event-based random processes.

MATERIALS AND METHODS

Activation detection is formulated in terms of time coupling between (1) the observed sequence of hemodynamic response onset (HRO) events detected in the voxel's fMRI signal and (2) the "hidden" sequence of task-induced neural activation onset (NAO) events underlying the HROs. Both event sequences are modeled within a single HSMESM. The resulting brain activation model is trained to automatically detect neural activity embedded in the input fMRI data set under analysis. The data sets considered in this article are threefold: synthetic epoch-related, real epoch-related (auditory lexical processing task), and real event-related (oddball detection task) fMRI data sets.

RESULTS

Synthetic data: Activation detection results demonstrate the superiority of the HSMESM mapping method with respect to a standard implementation of the statistical parametric mapping (SPM) approach. They are also very close, sometimes equivalent, to those obtained with an "ideal" implementation of SPM in which the activation patterns synthesized are reused for analysis. The HSMESM method appears clearly insensitive to timing variations of the hemodynamic response and exhibits low sensitivity to fluctuations of its shape (unsustained activation during task). Real epoch-related data: HSMESM activation detection results compete with those obtained with SPM, without requiring any prior definition of the expected activation patterns thanks to the unsupervised character of the HSMESM mapping approach. Along with activation maps, the method offers a wide range of additional fMRI analysis functionalities, including activation lag mapping, activation mode visualization, and hemodynamic response function analysis. Real event-related data: Activation detection results confirm and validate the overall strategy that consists in focusing the analysis on the transients, time-localized events that are the HROs.

CONCLUSION

All the experiments performed on synthetic and real fMRI data demonstrate the relevance of HSMESMs in fMRI brain mapping. In particular, the statistical character of these models, along with their learning and generalizing abilities are of particular interest when dealing with strong variabilities of the active fMRI signal across time, space, experiments, and subjects.