Single-trial laser-evoked potentials feature extraction for prediction of pain perception

Pain is a highly subjective experience, and the availability of an objective assessment of pain perception would be of great importance for both basic and clinical applications. The objective of the present study is to develop a novel approach to extract pain-related features from single-trial laser-evoked potentials (LEPs) for classification of pain perception. The single-trial LEP feature extraction approach combines a spatial filtering using common spatial pattern (CSP) and a multiple linear regression (MLR). The CSP method is effective in separating laser-evoked EEG response from ongoing EEG activity, while MLR is capable of automatically estimating the amplitudes and latencies of N2 and P2 from single-trial LEP waveforms. The extracted single-trial LEP features are used in a Naïve Bayes classifier to classify different levels of pain perceived by the subjects. The experimental results show that the proposed single-trial LEP feature extraction approach can effectively extract pain-related LEP features for achieving high classification accuracy.published_or_final_versio

Normalization of Pain-Evoked Neural Responses Using Spontaneous EEG Improves the Performance of EEG-Based Cross-Individual Pain Prediction

An effective physiological pain assessment method that complements the gold standard of self-report is highly desired in pain clinical research and practice. Recent studies have shown that pain-evoked electroencephalography (EEG) responses could be used as a readout of perceived pain intensity. Existing EEG-based pain assessment is normally achieved by cross-individual prediction (i.e., to train a prediction model from a group of individuals and to apply the model on a new individual), so its performance is seriously hampered by the substantial inter-individual variability in pain-evoked EEG responses. In this study, to reduce the inter-individual variability in pain-evoked EEG and to improve the accuracy of cross-individual pain prediction, we examined the relationship between pain-evoked EEG, spontaneous EEG, and pain perception on a pain EEG dataset, where a large number of laser pulses (>100) with a wide energy range were delivered. Motivated by our finding that an individual’s pain-evoked EEG responses is significantly correlated with his/her spontaneous EEG in terms of magnitude, we proposed a normalization method for pain-evoked EEG responses using one’s spontaneous EEG to reduce the inter-individual variability. In addition, a nonlinear relationship between the level of pain perception and pain-evoked EEG responses was obtained, which inspired us to further develop a new two-stage pain prediction strategy, a binary classification of low-pain and high-pain trials followed by a continuous prediction for high-pain trials only, both of which used spontaneous-EEG-normalized magnitudes of evoked EEG responses as features. Results show that the proposed normalization strategy can effectively reduce the inter-individual variability in pain-evoked responses, and the two-stage pain prediction method can lead to a higher prediction accuracy.published_or_final_versio

Decoding Subjective Intensity of Nociceptive Pain from Pre-stimulus and Post-stimulus Brain Activities

Pain is a highly subjective experience. Self-report is the gold standard for pain assessment in clinical practice, but it may not be available or reliable in some populations. Neuroimaging data, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have the potential to be used to provide physiology-based and quantitative nociceptive pain assessment tools that complements self-report. However, existing neuroimaging-based nociceptive pain assessments only rely on the information in pain-evoked brain activities, but neglect the fact that the perceived intensity of pain is also encoded by ongoing brain activities prior to painful stimulation. Here, we proposed to use machine learning algorithms to decode pain intensity from both pre-stimulus ongoing and post-stimulus evoked brain activities. Neural features that were correlated with intensity of laser-evoked nociceptive pain were extracted from high-dimensional pre- and post-stimulus EEG and fMRI activities using partial least-squares regression (PLSR). Further, we used support vector machine (SVM) to predict the intensity of pain from pain-related time-frequency EEG patterns and BOLD-fMRI patterns. Results showed that combining predictive information in pre- and post-stimulus brain activities can achieve significantly better performance in classifying high-pain and low-pain and in predicting the rating of perceived pain than only using post-stimulus brain activities. Therefore, the proposed pain prediction method holds great potential in basic research and clinical applications.published_or_final_versio

Attentional modulations of pain perception: evidence from laser evoked potentials

This thesis aims to provide a contribution to the current neurophysiological and psychophysiological understanding of nociception and pain processing in humans. The introduction of high-power, radiant heat stimulators (lasers) in sensory physiology has revolutionised the study of the nociceptive system. Laser pulses activate Aδ and/or C skin nociceptors selectively, i.e. without coactivating deeper, tactile mechanoreceptors, and elicit brain responses that can be detected using electroencephalography, and are called laser-evoked potentials (LEP). This was the technique applied in the two experimental studies reported in the present thesis work. The doctoral dissertation is organized in five chapters. Chapter 1 – Introduction - defines the concepts of nociception and pain. It also provides an introduction to the event related potential technique (ERP), a description of basic biophysics and neurophysiology related to LEP recording, followed by a literature review of its related cortical generators. In addition, the Chapter attempts to draw an elementary parallel between LEPs and other EPs elicited by stimuli belonging to other sensory modalities. Chapter 2 – Determinants of vertex potentials – describes the determinants of neural processes of pain perception and support their interpretation through a neurocognitive model of attention. The mechanism of attention allows allocating resources for selection and integration of this process with working memory requirements. More in detail, cognitive science suggested that the attention mechanism can be divided into two categories: stimulus-driven (or ‘bottom-up’) and goal-directed (or ‘top-down’). ‘Top-down’ and ‘bottom-up’ are treated as key interpretative categories to explain the findings reported in this thesis. Infact, they are metaphors which are used to represent information processing in a hierarchical fashion, where lower levels of processing would rely on the physical features of the stimulus while higher levels would involve comparisons with information stored in memory, selection of relevant information in competition and response to the stimulus. A review of selected literature in the field or ERP studies of sensory processing is provided and interpreted within this framework. The thesis aims to contribute to the understanding of both ‘bottom-up’ and ‘top-down’ mechanisms of attention during nociceptive processing, with two distinct experiments. Chapter 3 – Contribution to the analysis of ‘bottom-up features: “Dishabituation of laser-evoked EEG responses: dissecting the effect of certain and uncertain changes in stimulus modality” - presents a study where the hypothesis that a change of modality (from auditory to nociceptive and vicerversa, rather than no change at all) can significantly modulate brain responses (no matter the subjects expectation of this change) has been tested. The results of this study bring support for a determinant role of saliency (here modulated by the novelty introduced by a change in the stimulus modality) in affecting brain responses to the sensory input. Chapter 4 - Hypnotic modulation of sensory and affective dimensions of pain: a top-down signature on pain experience - introduces a study where hypnotic suggestions were used to draw subject’s attention either on intensity or on unpleasantness of pain perception. Thus, the study aimed to investigate whether this manipulation could induce a dissociation between this two measure of subjective experience and whether LEP could reflect the role of focused attention and expectation in indexing changes of subjective feeling. The results are discussed according to previous literature and to a neurocognitive model of pain processing as observed during an altered state of consciousness known to heighten the fronto-parietal network of sustained attention. In Chapter 5 - General discussion - the findings related to these two different research lines are integrated and discussed considering the existing theoretical accounts. The critical assumption is that the understanding of pain processing would largely benefit from the application of an attention-driven interpretative framework within which can be included different theoretical-epistemological views concerning (II) the Bayesian inference in perception, (III) the motivational account of pain monitoring and control, (IV) the neuroanatomy of homeostatic feeling of body integrity and self-regulation. As conclusive remark, the work presented in this thesis wish to highlight the importance of a renewed concept of ‘pain matrix’, based on its function of potential threat detector and action planner, in order to preserve the integrity of the body. In addition, the interpretation of pain as homeostatic-motivational force naturally carries us to consider the ‘pain matrix’ not as a sensory-specific cortical network but rather as an action-specific network, representing the activity by which the individual identifies and responds purposefully to a sudden, potential threat inside or outside of the body

Multimodal assessment of neonatal pain

Pain assessment is critical to prevent suffering and harm in infants admitted to the neonatal care unit. As pain is a subjective experience, its assessment in nonverbal infants relies on surrogate measures. Current infant pain assessment tools that are based on behaviour and autonomic nervous system measurements lack face validity — they are unlikely to reflect pain in all its dimensions. In recent years, EEG-derived measures of pain have been developed in late preterm and term infants. Multimodal tools which include these cerebral measurements are conceptually more appropriate to measure pain. Yet, their use is still limited to specific research applications. This thesis focuses on outstanding questions that need to be addressed in order to advance the development of multimodal pain assessment tools that incorporate cerebral measurements. In the first part of this thesis, I focus on the characterisation of preterm infants’ noxious-evoked responses and their development. Across several modalities, premature infants have dampened or altered responsiveness compared to term infants, and it is uncertain if these responses can be reliably discriminated from tactile-evoked responses. In particular, a discriminative pattern of noxious-evoked EEG activity that is present in term infants, is unlikely to be present in preterm infants. In addition, it is unclear how noxious-evoked responses, especially brainderived responses, change with age. In this thesis, I use a classification model to show that infants aged 28–40 weeks postmenstrual age display discriminable multimodal responses to a noxious clinical procedure and a tactile control procedure, and I provide examples of how a such a model could be used in clinical trials of analgesics. I show that noxious-evoked responses change magnitude and morphology across this age range, and that discriminative brain activity emerges in early prematurity. In the second part of this thesis, I focus on improving the neuroscientific validity of a noxious-evoked EEG response measured at the cot-side, as the spatial neural correlates of these responses are still poorly understood. I present an EEG-fMRI pilot study to investigate the spatial neural correlates of inter-individual differences in noxious-evoked EEG responses and provide recommendations for a larger follow-up study. Overall, this thesis provides a characterisation of infants’ noxious-evoked responses and their development across multiple modalities, a crucial next step in improving multimodal neonatal pain assessment

Influence of Individual Differences in fMRI-Based Pain Prediction Models on Between-Individual Prediction Performance

Decoding subjective pain perception from functional magnetic resonance imaging (fMRI) data using machine learning technique is gaining a growing interest. Despite the well-documented individual differences in pain experience and brain responses, it still remains unclear how and to what extent these individual differences affect the performance of between-individual fMRI-based pain prediction. The present study is aimed to examine the relationship between individual differences in pain prediction models and between-individual prediction error, and, further, to identify brain regions that contribute to between-individual prediction error. To this end, we collected and analyzed fMRI data and pain ratings in a laser-evoked pain experiment. By correlating different types of individual difference metrics with between-individual prediction error, we are able to quantify the influence of these individual differences on prediction performance and reveal a set of brain regions whose activities are related to prediction error. Interestingly, we found that the precuneus, which does not have predictive capability to pain, could also affect the prediction error. This study elucidates the influence of interindividual variability in pain on the between-individual prediction performance, and the results will be useful for the design of more accurate and robust fMRI-based pain prediction models

Human brain mechanisms of pain perception and regulation in health and disease

Context The perception of pain due to an acute injury or in clinical pain states undergoes substantial processing at supraspinal levels. Supraspinal, brain mechanisms are increasingly recognized as playing a major role in the representation and modulation of pain experience. These neural mechanisms may then contribute to interindividual variations and disabilities associated with chronic pain conditions. Objective To systematically review the literature regarding how activity in diverse brain regions creates and modulates the experience of acute and chronic pain states, emphasizing the contribution of various imaging techniques to emerging concepts. Data Sources MEDLINE and PRE‐MEDLINE searches were performed to identify all English‐language articles that examine human brain activity during pain, using hemodynamic (PET, fMRI), neuroelectrical (EEG, MEG) and neurochemical methods (MRS, receptor binding and neurotransmitter modulation), from January 1, 1988 to March 1, 2003. Additional studies were identified through bibliographies. Study Selection Studies were selected based on consensus across all four authors. The criteria included well‐designed experimental procedures, as well as landmark studies that have significantly advanced the field. Data Synthesis Sixty‐eight hemodynamic studies of experimental pain in normal subjects, 30 in clinical pain conditions, and 30 using neuroelectrical methods met selection criteria and were used in a meta‐analysis. Another 24 articles were identified where brain neurochemistry of pain was examined. Technical issues that may explain differences between studies across laboratories are expounded. The evidence for and the respective incidences of brain areas constituting the brain network for acute pain are presented. The main components of this network are: primary and secondary somatosensory, insular, anterior cingulate, and prefrontal cortices (S1, S2, IC, ACC, PFC) and thalamus (Th). Evidence for somatotopic organization, based on 10 studies, and psychological modulation, based on 20 studies, is discussed, as well as the temporal sequence of the afferent volley to the cortex, based on neuroelectrical studies. A meta‐analysis highlights important methodological differences in identifying the brain network underlying acute pain perception. It also shows that the brain network for acute pain perception in normal subjects is at least partially distinct from that seen in chronic clinical pain conditions and that chronic pain engages brain regions critical for cognitive/emotional assessments, implying that this component of pain may be a distinctive feature between chronic and acute pain. The neurochemical studies highlight the role of opiate and catecholamine transmitters and receptors in pain states, and in the modulation of pain with environmental and genetic influences. Conclusions The nociceptive system is now recognized as a sensory system in its own right, from primary afferents to multiple brain areas. Pain experience is strongly modulated by interactions of ascending and descending pathways. Understanding these modulatory mechanisms in health and in disease is critical for developing fully effective therapies for the treatment of clinical pain conditions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90300/1/j.ejpain.2004.11.001.pd