Prediction of postoperative opioid analgesia using clinical-experimental parameters and electroencephalography

Background: Opioids are often used for pain treatment, but the response is often insufficient and dependent on e.g. the pain condition, genetic factors and drug class. Thus, there is an urgent need to identify biomarkers to enable selection of the appropriate drug for the individual patient, a concept known as personalized medicine. Quantitative sensory testing (QST) and clinical parameters can provide some guidance for response, but better and more objective biomarkers are urgently warranted. Electroencephalography (EEG) may be suitable since it assesses the central nervous system where opioids mediate their effects. Methods: Clinical parameters, QST and EEG (during rest and tonic pain) was recorded from patients the day prior to total hip replacement surgery. Postoperative pain treatment was performed using oxycodone and piritramide as patient-controlled analgesia. Patients were stratified into responders and non-responders based on pain ratings 24 h post-surgery. Parameters were analysed using conventional group-wise statistical methods. Furthermore, EEG was analysed by machine learning to predict individual response. Results: Eighty-one patients were included, of which 51 responded to postoperative opioid treatment (30 non-responders). Conventional statistics showed that more severe pre-existing chronic pain was prevalent among non-responders to opioid treatment (p = 0.04). Preoperative EEG analysis was able to predict responders with an accuracy of 65% (p = 0.009), but only during tonic pain. Conclusions: Chronic pain grade before surgery is associated with the outcome of postoperative pain treatment. Furthermore, EEG shows potential as an objective biomarker and might be used to predict postoperative opioid analgesia. Significance: The current clinical study demonstrates the viability of EEG as a biomarker and with results consistent with previous experimental results. The combined method of machine learning and electroencephalography offers promising results for future developments of personalized pain treatment.</p

Use of dry-electroencephalogram and support vector for objective pain assessment

Our primary goal was to objectively quantify pain. The experiment we designated for this task was via dry electroencephalography (EEG) in conjunction with a support vector machine classifier (SVM). Normal gel-based electrode EEG has been validated as reliable in pain measurement. Yet, to date, there are few documented trials that use dry-EEG for pain quantification. In addition, SVM classifiers have proven accurate when classifying pain intensity. Therefore, we believe EEG combined with SVM could increase the statistical power of pain assessment. However, due to the subjectivity of pain, currently clinicians mainly rely on verbal reports. This research could offer a method to objectively monitor pain, eliminate observer error and individualize treatment

Functional assessment of bidirectional cortical and peripheral neural control on heartbeat dynamics: A brain-heart study on thermal stress

The study of functional Brain-Heart Interplay (BHI) from non-invasive recordings has gained much interest in recent years. Previous endeavors aimed at understanding how the two dynamical systems exchange information, providing novel holistic biomarkers and important insights on essential cognitive aspects and neural system functioning. However, the interplay between cardiac sympathovagal and cortical oscillations still has much room for further investigation. In this study, we introduce a new computational framework for a functional BHI assessment, namely the Sympatho-Vagal Synthetic Data Generation Model, combining cortical (electroencephalography, EEG) and peripheral (cardiac sympathovagal) neural dynamics. The causal, bidirectional neural control on heartbeat dynamics was quantified on data gathered from 26 human volunteers undergoing a cold-pressor test. Results show that thermal stress induces heart-to-brain functional interplay sustained by EEG oscillations in the delta and gamma bands, primarily originating from sympathetic activity, whereas brain-to-heart interplay originates over central brain regions through sympathovagal control. The proposed methodology provides a viable computational tool for the functional assessment of the causal interplay between cortical and cardiac neural control

Functional assessment of bidirectional cortical and peripheral neural control on heartbeat dynamics: A brain-heart study on thermal stress

The study of functional Brain-Heart Interplay (BHI) from non-invasive recordings has gained much interest in recent years. Previous endeavors aimed at understanding how the two dynamical systems exchange information, providing novel holistic biomarkers and important insights on essential cognitive aspects and neural system functioning. However, the interplay between cardiac sympathovagal and cortical oscillations still has much room for further investigation. In this study, we introduce a new computational framework for a functional BHI assessment, namely the Sympatho-Vagal Synthetic Data Generation Model, combining cortical (electroencephalography, EEG) and peripheral (cardiac sympathovagal) neural dynamics. The causal, bidirectional neural control on heartbeat dynamics was quantified on data gathered from 26 human volunteers undergoing a cold-pressor test. Results show that thermal stress induces heart-to-brain functional interplay sustained by EEG oscillations in the delta and gamma bands, primarily originating from sympathetic activity, whereas brain-to-heart interplay originates over central brain regions through sympathovagal control. The proposed methodology provides a viable computational tool for the functional assessment of the causal interplay between cortical and cardiac neural control

THE MANY WAYS OF WAKING UP FROM SLEEP - MOVING FORWARD THE ANALYSIS OF SLEEP MICROARCHITECTURE

One of the defining characteristics of sleep is that it is readily reversible towards wakefulness. This is exemplified in the common daily experience of waking up in the morning. My thesis studies sleep-wake transitions that are equally common and frequent, yet often not consciously perceived and neglected as random sleep perturbations of minor significance. Using mice as an experimental species, I find that healthy non-rapid-eye-movement sleep (NREMS), also named deep restorative sleep, is a dynamic brain state showing defined, periodically recurring moments of fragility. During these, diverse types of brief arousal-like events with various combinations of physiological correlates appear, including global or local cortical activation, muscle activity, and heart rate changes. Using a mice model of chronic neuropathic pain, I find that the rules I have identified in healthy sleep serve to identify previously unrecognized sleep disruptions that could contribute to sleep complaints of chronic pain patients. The experimental and analytical methods I have developed in these studies also helped in the identification of the neuronal basis of the fragility periods of NREM sleep. Together, my studies offer novel insights and analytical tools for the study of sleep-wake transitions and their perturbance in pathological conditions linked to sensory discomfort. More specifically, my work departed from recent findings that NREMS in mice is divided in recurring periods of sleep fragility at frequencies ~0.02 Hz, characterized by heightened arousability. Through analyzing the temporal distribution of brief arousal events termed microarousals, I hypothesized that these fragility periods could serve a time raster for the probing of spontaneous sleep perturbations. Motivated by the question of how sensory discomfort caused by pain affects sleep, I have used the spared nerve injury (SNI) model, which consists in the injury of two of the 3 branches of the sciatic nerve. I found that the role of fragility periods in timing spontaneous arousals is highly useful to identify sleep disruptions not commonly detected with standard polysomnographic measures. Thus, by scrutinizing the fragility periods of NREMS in the SNI mice, I discovered an overrepresentation of a novel form of local perturbation within the hindlimb primary somatosensory cortex (S1HL), accompanied by heart rate increases. In addition, I showed that SNI animals woke up more frequently facing external stimuli, using closed-loop methods targeting specifically the fragility or continuity periods. These findings led me to propose that chronic pain-related sleep complaints may arise primarily from a perturbed arousability. The closed-loop techniques to probe arousability could be transferred to interrogate neuronal mechanisms underlying NREMS fragility, leading to the recognition that intrusion of wake-related activity into NREMS is a previously underappreciated mechanism controlling sleep fragility and architecture. Overall, I present my thesis to advance the view on NREMS as a dynamic heterogeneous state of which insights into its neuronal mechanisms, and its physio- and pathophysiological manifestations in animal models should be key to formulate testable hypotheses aimed to cure the suffering of sleep disorder in human. -- Une des caractéristiques qui définit le sommeil, est que l’on peut rapidement retourner à un état d’éveil. De fait, nous l’expérimentons chaque matin au réveil. Ma thèse étudie les transitions sommeil-éveil qui, bien que fréquentes, sont souvent non consciemment perçues et traitées comme des perturbations sans importance et aléatoires du sommeil. En utilisant la souris comme modèle expérimental, je montre que le sommeil sans mouvements rapides des yeux (NREMS), également appelé le sommeil profond et réparateur, est un état cérébral dynamique composé de périodes discrètes et récurrentes de fragilité face à des stimuli externe. Pendant celles-ci, plusieurs types d’évènements associés à des éveils brefs apparaissent, combinant activation corticale, activité musculaire et/ou une hausse des battements cardiaques. Je démontre que la compréhension des transitions sommeil-éveil physiologiques s’avère utile pour étudier le sommeil de souris souffrant de douleurs neuropathiques chroniques. Ces souris présentent un nouveau type de perturbations locales lors du sommeil, qui pourraient possiblement expliquer une partie des plaintes de mauvais sommeil exprimées par les patients souffrant de douleurs chroniques. Les méthodes analytiques et expérimentales que j’ai développées dans ces études ont aussi aidé à l’identification des bases neuronales de la genèse des périodes de fragilités du sommeil NREM. En somme, mes études offrent des connaissances inédites et des méthodes d’analyses pour l’étude des transitions sommeil-éveil et de leurs perturbations en conditions pathologiques. Une étude récente du laboratoire a montré que le sommeil NREM est divisé en périodes de fragilité alternant avec des périodes de non-fragilité (continuité), environ toutes les 50 secondes ce qui donne une fréquence de 0.02 Hz. Les périodes de fragilité sont caractérisées par une hausse de « l’éveillabilité » ou propension à s’éveiller. Ma première observation est que les éveils brefs, couramment appelés micro-réveils, présentent une distribution temporelle hautement restreinte aux périodes de fragilité. Ainsi, j’ai émis l’hypothèse que ces périodes pourraient servir de moments spécialement choisis par le cerveau pour la mesure de potentielles perturbations spontanées. Motivé par la question de comment les douleurs chroniques perturbent le sommeil, je l’ai analysé chez un modèle de souris de douleurs neuropathique, le modèle de d’épargne du nerf sural (SNI). Le rôle des périodes de fragilité à restreindre les micro- réveils s’est avéré très utile pour détecter de nouvelles formes de réaction à des perturbations qui ne sont pas évidentes par des analyses classiques du sommeil. En effet, spécifiquement pendant ces périodes de fragilité, j’ai découvert une sur-représentation d’un nouveau type d’éveil local confiné au cortex somatosensoriel primaire et accompagné d’une hausse du rythme cardiaque. De plus, en utilisant de nouvelles méthodes basées sur des boucles-fermées, j’ai démontré que les souris SNI se réveillaient plus fréquemment que leurs contrôles en faisant face à des stimuli externes. Sur la base de ces découvertes, je propose que les plaintes de mauvais sommeil chez les patients souffrant de douleurs chroniques puissent prendre leur source dans une éveillabilité perturbée. Les méthodes de boucles-fermées pour analyser l’éveillabilité a aussi pu être transférée pour l’étude optogénétique des mécanismes neuronaux à la base de la fragilité du sommeil NREM. Cela a mené à la reconnaissance que l’intrusion d’activité normalement associée à l’éveil dans le sommeil est un mécanisme de contrôle de sa fragilité et de son architecture souvent ignoré dans le domaine. En somme, ma thèse permet une avancée de notre vision du sommeil NREM comme étant un état dynamique et hétérogène dont les mécanismes neuronaux sous-jacent, en conditions normales et pathogéniques, sont clefs pour la formulation d’hypothèses testables visant à la guérison des patients souffrant de troubles du sommeil

Impaired brainstem and thalamic high-frequency oscillatory EEG activity in migraine between attacks

INTRODUCTION: We investigated whether interictal thalamic dysfunction in migraine without aura (MO) patients is a primary determinant or the expression of its functional disconnection from proximal or distal areas along the somatosensory pathway. METHODS: Twenty MO patients and twenty healthy volunteers (HVs) underwent an electroencephalographic (EEG) recording during electrical stimulation of the median nerve at the wrist. We used the functional source separation algorithm to extract four functionally constrained nodes (brainstem, thalamus, primary sensory radial, and primary sensory motor tangential parietal sources) along the somatosensory pathway. Two digital filters (1-400 Hz and 450-750 Hz) were applied in order to extract low- (LFO) and high- frequency (HFO) oscillatory activity from the broadband signal. RESULTS: Compared to HVs, patients presented significantly lower brainstem (BS) and thalamic (Th) HFO activation bilaterally. No difference between the two cortical HFO as well as in LFO peak activations between the two groups was seen. The age of onset of the headache was positively correlated with HFO power in the right brainstem and thalamus. CONCLUSIONS: This study provides evidence for complex dysfunction of brainstem and thalamocortical networks under the control of genetic factors that might act by modulating the severity of migraine phenotype

Recent Applications in Graph Theory

Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks

Coherence analysis: Methods, solutions and problems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A coherence function is a measure of the correlation of two signals and may be used as a measure for functional relationship between brain areas. In studying functional relationships, referenced EEG (REEG) coherence analysis yields important new aspects of brain activities, which complement the data obtained by power spectral analysis. However, REEG-based coherence tends to show a false high value due to volume conduction from un correlated sources (VCUS). Existing signal processing methods address this issue using a Fourier coherence function of scalp Laplacian. Although this method has been proved useful to reveal correlation between EEG signals with minimum VCUS effects, it only provides frequency-domain analysis. Since EEG signals are highly non-stationary, it is more appropriate to use time-frequency methods for coherence analysis of scalp Laplacian. Thus this research applies the wavelet transform on coherence analysis of scalp Laplacian. To verify our technique, already recorded EEG data of event related potentials were obtained from a study of two large groups of alcoholic and abstinent alcoholic subjects, performing visual picture-recognition tasks. The proposed coherence method successfully detected time-frequency correlation between EEG signals with minimum VCUS effects. It showed significant spatial specificity and revealed detailed coherence patterns. Some new important results regarding time-frequency characteristics of VCUS effects on wavelet and short-time Fourier transform (STFT) coherence analysis of REEG signals were deduced. The proposed coherence method was also compared to a conventional wavelet coherence method of REEG signals in the study of coherence difference between coherences of alcoholic and abstinent alcoholic EEG signals. Results of this study provided substantial evidence that VCUS effects are not additive and therefore can not be ignored in comparison of different brain states between groups of subjects

Towards the Objective Identification of the Presence of Pain Based on Electroencephalography Signals’ Analysis: A Proof-of-Concept

This proof-of-concept study explores the potential of developing objective pain identification based on the analysis of electroencephalography (EEG) signals. Data were collected from participants living with chronic fibromyalgia pain (n = 4) and from healthy volunteers (n = 7) submitted to experimental pain by the application of capsaicin cream (1%) on the right upper trapezius. This data collection was conducted in two parts: (1) baseline measures including pain intensity and EEG signals, with the participant at rest; (2) active measures collected under the execution of a visuo-motor task, including EEG signals and the task performance index. The main measure for the objective identification of the presence of pain was the coefficient of variation of the upper envelope (CVUE) of the EEG signal from left fronto-central (FC5) and left temporal (T7) electrodes, in alpha (8–12 Hz), beta (12–30 Hz) and gamma (30–43 Hz) frequency bands. The task performance index was also calculated. CVUE (%) was compared between groups: those with chronic fibromyalgia pain, healthy volunteers with “No pain” and healthy volunteers with experimentally-induced pain. The identification of the presence of pain was determined by an increased CVUE in beta (CVUEβ) from the EEG signals captured at the left FC5 electrode. More specifically, CVUEβ increased up to 20% in the pain condition at rest. In addition, no correlation was found between CVUEβ and pain intensity or the task performance index. These results support the objective identification of the presence of pain based on the quantification of the coefficient of variation of the upper envelope of the EEG signal