Biomarkers for visceral hypersensitivity identified by classification of electroencephalographic frequency alterations

Correlations between EEG and intestinal electrical stimulation

Many diseases affect the autonomous nervous system and the central nervous system simultaneously, for example Parkinson's disease or irritable bowel syndrome. To study neurophysiologic interactions between the intestinal electrical activity and the electroencephalography (EEG) pattern of the brain, we combined intestinal electrical stimulation (IES) and non-invasive telemetric full-band DC EEG recordings in an acute pig-model. Intestinal motility was monitored with accelerometers. Brain activity was analyzed with regard to network driven phenomena like phase amplitude coupling (PAC) within two time-windows: 1 min after IES (early response) and 3 min after stimulation (late response). Here we present the results for two stimulation sites (small intestine, colon) and two parietal scalp-EEG channels (right and left somatosensory cortex region). Electrical stimulation consisted of a 30 or 130 Hz pulse. In summary, the PAC modulation index at a parietal EEG recording position is decreased after IES. This effect is in line with an inhibitory effect of our IES protocol regarding peristalsis. The surprisingly strong effects of IES on network driven EEG patterns may be translated into new therapeutic techniques and/or diagnostic tools in the future. Furthermore, analytic tools, operating on sparse datasets, may be ideally suited for the integration in implantable intestinal pacemakers as feedback system.

Systematic Review of the Effectiveness of Machine Learning Algorithms for Classifying Pain Intensity, Phenotype or Treatment Outcomes Using Electroencephalogram Data

Recent attempts to utilize machine learning (ML) to predict pain-related outcomes from Electroencephalogram (EEG) data demonstrate promising results. The primary aim of this review was to evaluate the effectiveness of ML algorithms for predicting pain intensity, phenotypes or treatment response from EEG. Electronic databases MEDLINE, EMBASE, Web of Science, PsycINFO and The Cochrane Library were searched. A total of 44 eligible studies were identified, with 22 presenting attempts to predict pain intensity, 15 investigating the prediction of pain phenotypes and seven assessing the prediction of treatment response. A meta-analysis was not considered appropriate for this review due to heterogenos methods and reporting. Consequently, data were narratively synthesized. The results demonstrate that the best performing model of the individual studies allows for the prediction of pain intensity, phenotypes and treatment response with accuracies ranging between 62 to 100%, 57 to 99% and 65 to 95.24%, respectively. The results suggest that ML has the potential to effectively predict pain outcomes, which may eventually be used to assist clinical care. However, inadequate reporting and potential bias reduce confidence in the results. Future research should improve reporting standards and externally validate models to decrease bias, which would increase the feasibility of clinical translation. PERSPECTIVE: This systematic review explores the state-of-the-art machine learning methods for predicting pain intensity, phenotype or treatmentresponse from EEG data. Results suggest that machine learning may demonstrate clinical utility, pending further research and development. Areas for improvement, including standardized processing, reporting and the need for better methodological assessment tools, are discussed.

Adapted wavelet transform improves time-frequency representations: a study of auditory elicited P300-like event-related potentials in rats

OBJECTIVE

Active auditory oddball paradigms are simple tone discrimination tasks used to study the P300 deflection of event-related potentials (ERPs). These ERPs may be quantified by time-frequency analysis. As auditory stimuli cause early high frequency and late low frequency ERP oscillations, the continuous wavelet transform (CWT) is often chosen for decomposition due to its multi-resolution properties. However, as the conventional CWT traditionally applies only one mother wavelet to represent the entire spectrum, the time-frequency resolution is not optimal across all scales. To account for this, we developed and validated a novel method specifically refined to analyse P300-like ERPs in rats.

APPROACH

An adapted CWT (aCWT) was implemented to preserve high time-frequency resolution across all scales by commissioning of multiple wavelets operating at different scales. First, decomposition of simulated ERPs was illustrated using the classical CWT and the aCWT. Next, the two methods were applied to EEG recordings obtained from prefrontal cortex in rats performing a two-tone auditory discrimination task.

MAIN RESULTS

While only early ERP frequency changes between responses to target and non-target tones were detected by the CWT, both early and late changes were successfully described with strong accuracy by the aCWT in rat ERPs. Increased frontal gamma power and phase synchrony was observed particularly within theta and gamma frequency bands during deviant tones.

SIGNIFICANCE

The study suggests superior performance of the aCWT over the CWT in terms of detailed quantification of time-frequency properties of ERPs. Our methodological investigation indicates that accurate and complete assessment of time-frequency components of short-time neural signals is feasible with the novel analysis approach which may be advantageous for characterisation of several types of evoked potentials in particularly rodents.

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.

Multivariate analysis of single-sweep evoked brain potentials for pharmaco-electroencephalography

BACKGROUND AND AIMS

Current findings on altered evoked potentials (EPs) caused by morphine are based on common alterations for a group of subjects after drug administration. However, this ignores the analysis of individual responses, which may explain the clinical differences in efficacy. Therefore, we explored the individual responses to morphine in terms of the altered single-sweep characteristics in a placebo-controlled crossover study. To account for multifactorial mechanisms, several characteristics were assessed simultaneously by multivariate pattern analysis (MVPA).

METHODS

EPs were recorded from 62 channels and obtained before and after morphine and placebo administration during repeated electrical stimulations of the oesophagus in 12 healthy males. Additionally, the pain detection threshold was recorded to reflect the subjective analgesic effect in each subject. The characteristics of the sweeps were extracted by a multivariate matching pursuit algorithm with Gabor atoms implemented with a variable amplitude and constant phase across the sweeps. The single-sweep amplitudes were used as input to an MVPA algorithm to discriminate individual responses. The accuracy of the MVPA for each individual subject was used for correlation analysis of the analgesic effect.

RESULTS

The mean classification accuracy when discriminating pre- and posttreatment morphine responses was 72% (p = 0.01). The individual classification accuracy was positively correlated to the analgesic effect of morphine (p = 0.03). Furthermore, the 2 posttreatment responses were classified and validated by the classification of the 2 pretreatment responses (p = 0.001).

CONCLUSIONS

The alterations in the single-sweep EPs after morphine reflect the analgesic effect. The MVPA approach is a novel methodology for monitoring the individual efficacy of analgesics.

Abnormal neuronal response to rectal and anal stimuli in patients with idiopathic fecal incontinence

BACKGROUND

The pathophysiology behind idiopathic fecal incontinence (IFI) is poorly understood. We hypothesized abnormal sensory pathways along the brain-gut axis as a key player in this disease, reflected in cortical evoked potentials (CEP) from mechanical stimuli of the rectum and the anal canal.

METHODS

CEPs were recorded during repeated rapid balloon distensions of the rectum and anal canal in 19 women with IFI (mean age: 60 ± 14, mean Wexner score: 14.7 ± 2.9) and in 19 healthy women (mean age: 56 ± 11, mean Wexner score: 1.1 ± 1.3). Latencies, amplitudes and topography of CEPs elicited by rectal distension were compared between the groups. CEPs from both rectal and anal distensions were examined using spectral band analysis of single sweeps determining the relative amplitude of five spectral bands as a proxy of neuronal processing.

KEY RESULTS

Compared to controls IFI patients had prolonged latency of CEPs from rectal distension by up to 27% (p < 0.001) while amplitudes and topography were similar (all p > 0.7 and all p > 0.23). Spectral analysis of CEPs from rectal distensions showed no difference (all p > 0.1) between groups. However, analysis of CEPs following distension of the anal canal resulted in abnormally low activity in beta (8-12 Hz; p < 0.001) band and high activity in the gamma (32-70 Hz; p = 0.04) band in patients.

CONCLUSIONS & INFERENCES

IFI seems to be associated with impaired ano-rectal sensory functions in both the afferent fibers to the brain and the cortical processing of anal sensory pathways. This may play a central role for the pathogenesis of IFI.

A novel approach to pharmaco-EEG for investigating analgesics: assessment of spectral indices in single-sweep evoked brain potentials

AIMS

To compare results from analysis of averaged and single-sweep evoked brain potentials (EPs) by visual inspection and spectral analysis in order to identify an objective measure for the analgesic effect of buprenorphine and fentanyl.

METHODS

Twenty-two healthy males were included in a randomized study to assess the changes in EPs after 110 sweeps of painful electrical stimulation to the median nerve following treatment with buprenorphine, fentanyl or placebo patches. Bone pressure, cutaneous heat and electrical pain ratings were assessed. EPs and pain assessments were obtained before drug administration, 24, 48, 72 and 144 h after beginning of treatment. Features from EPs were extracted by three different approaches: (i) visual inspection of amplitude and latency of the main peaks in the average EPs, (ii) spectral distribution of the average EPs and (iii) spectral distribution of the EPs from single-sweeps.

RESULTS

Visual inspection revealed no difference between active treatments and placebo (all P > 0.05). Spectral distribution of the averaged potentials showed a decrease in the beta (12-32 Hz) band for fentanyl (P = 0.036), which however did not correlate with pain ratings. Spectral distribution in the single-sweep EPs revealed significant increases in the theta, alpha and beta bands for buprenorphine (all P < 0.05) as well as theta band increase for fentanyl (P = 0.05). For buprenorphine, beta band activity correlated with bone pressure and cutaneous heat pain (both P = 0.04, r = 0.90).

CONCLUSION

In conclusion single-sweep spectral band analysis increases the information on the response of the brain to opioids and may be used to identify the response to analgesics.

Support vector regression correlates single-sweep evoked brain potentials to gastrointestinal symptoms in diabetes mellitus patients

Diabetes mellitus (DM) is a multi-factorial and complex disease causing autonomic neuropathy and gastrointestinal symptoms in some patients. The neural mechanisms behind these symptoms are poorly understood, but it is believed that both peripheral and central mechanisms are involved. To gain further knowledge of the central mechanisms, the aim of this study was to identify biomarkers for the altered brain activity in type-1 DM patients compared to healthy volunteers (HV), and to correlate the obtained biomarkers to clinical patient scores. The study included 14 DM patients and 15 HV, with brain activity recorded as multi-channel electroencephalography evoked brain potentials (EPs) elicited by painful electrical stimulations in the esophagus. The single-sweep EPs were decomposed by an optimized discrete wavelet transform (DWT), and averaged for each channel. The DWT features from the DM patients were discriminated from the HV by a support vector machine (SVM) applied in regression mode. For the optimal DWT, the discriminative features were extracted and the SVM regression value representing the overall alteration of the EP was correlated to the clinical scores. A classification performance of 86.2% (P=0.01) was obtained by applying a majority voting scheme to the 5 best performing channels. The biomarker was identified as decreased theta band activity. The regression value was correlated to symptoms reported by the patients (P=0.04). The methodology is an improvement of the present approach to study central mechanisms in diabetes mellitus, and may provide a future application for a clinical tool to optimize treatment in individual patients.

Unravelling the mystery of capsaicin: a tool to understand and treat pain

A large number of pharmacological studies have used capsaicin as a tool to activate many physiological systems, with an emphasis on pain research but also including functions such as the cardiovascular system, the respiratory system, and the urinary tract. Understanding the actions of capsaicin led to the discovery its receptor, transient receptor potential (TRP) vanilloid subfamily member 1 (TRPV1), part of the superfamily of TRP receptors, sensing external events. This receptor is found on key fine sensory afferents, and so the use of capsaicin to selectively activate pain afferents has been exploited in animal studies, human psychophysics, and imaging studies. Its effects depend on the dose and route of administration and may include sensitization, desensitization, withdrawal of afferent nerve terminals, or even overt death of afferent fibers. The ability of capsaicin to generate central hypersensitivity has been valuable in understanding the consequences and mechanisms behind enhanced central processing of pain. In addition, capsaicin has been used as a therapeutic agent when applied topically, and antagonists of the TRPV1 receptor have been developed. Overall, the numerous uses for capsaicin are clear; hence, the rationale of this review is to bring together and discuss the different types of studies that exploit these actions to shed light upon capsaicin working both as a tool to understand pain but also as a treatment for chronic pain. This review will discuss the various actions of capsaicin and how it lends itself to these different purposes.