Author Correction: Boundary effects of expectation in human pain perception.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Boundary effects of expectation in human pain perception.

Perception of sensory stimulation is influenced by numerous psychological variables. One example is placebo analgesia, where expecting low pain causes a painful stimulus to feel less painful. Yet, because pain evolved to signal threats to survival, it should be maladaptive for highly-erroneous expectations to yield unrealistic pain experiences. Therefore, we hypothesised that a cue followed by a highly discrepant stimulus intensity, which generates a large prediction error, will have a weaker influence on the perception of that stimulus. To test this hypothesis we collected two independent pain-cueing datasets. The second dataset and the analysis plan were preregistered ( https://osf.io/5r6z7/ ). Regression modelling revealed that reported pain intensities were best explained by a quartic polynomial model of the prediction error. The results indicated that the influence of cues on perceived pain decreased when stimulus intensity was very different from expectations, suggesting that prediction error size has an immediate functional role in pain perception.

Sensitivity to pain expectations: A Bayesian model of individual differences.

The thoughts and feelings people have about pain (referred to as 'pain expectations') are known to alter the perception of pain. However little is known about the cognitive processes that underpin pain expectations, or what drives the differing effect that pain expectations have between individuals. This paper details the testing of a model of pain perception which formalises the response to pain in terms of a Bayesian prior-to-posterior updating process. Using data acquired from a short and deception-free predictive cue task, it was found that this Bayesian model predicted ratings of pain better than other, simpler models. At the group level, the results confirmed two core predictions of predictive coding; that expectation alters perception, and that increased uncertainty in the expectation reduces its impact on perception. The addition of parameters relating to trait differences in pain expectation improved the fit of the model, suggesting that such traits play a significant role in perception above and beyond the influence of expectations triggered by predictive cues. When the model parameters were allowed to vary by participant, the model's fit improved further. This final model produced a characterisation of each individual's sensitivity to pain expectations. This model is relevant for the understanding of the cognitive basis of pain expectations and could potentially act as a useful tool for guiding patient stratification and clinical experimentation.

A comparison between the neural correlates of laser and electric pain stimulation and their modulation by expectation.

BACKGROUND: Pain is modulated by expectation. Event-related potential (ERP) studies of the influence of expectation on pain typically utilise laser heat stimulation to provide a controllable nociceptive-specific stimulus. Painful electric stimulation has a number of practical advantages, but is less nociceptive-specific. We compared the modulation of electric versus laser-evoked pain by expectation, and their corresponding pain-evoked and anticipatory ERPs. NEW METHOD: We developed understanding of recognised methods of laser and electric stimulation. We tested whether pain perception and neural activity induced by electric stimulation was modulated by expectation, whether this expectation elicited anticipatory neural correlates, and how these measures compared to those associated with laser stimulation by eliciting cue-evoked expectations of high and low pain in a within-participant design. RESULTS: Despite sensory and affective differences between laser and electric pain, intensity ratings and pain-evoked potentials were modulated equivalently by expectation, though ERPs only correlated with pain ratings in the laser pain condition. Anticipatory correlates differentiated pain intensity expectation to laser but not electric pain. COMPARISON WITH EXISTING METHOD: Previous studies show that laser-evoked potentials are modulated by expectation. We extend this by showing electric pain-evoked potentials are equally modulated by expectation, within the same participants. We also show a difference between the pain types in anticipation. CONCLUSIONS: Though laser-evoked potentials express a stronger relationship with pain perception, both laser and electric stimulation may be used to study the modulation of pain-evoked potentials by expectation. Anticipatory-evoked potentials are elicited by both pain types, but they may reflect different processes.

Temporal dissociation of salience and prediction error responses to appetitive and aversive taste.

The feedback-related negativity (FRN), a frontocentral ERP occurring 200-350 ms after emotionally valued outcomes, has been posited as the neural correlate of reward prediction error, a key component of associative learning. Recent evidence challenged this interpretation and has led to the suggestion that this ERP expresses salience instead. Here, we distinguish between utility prediction error and salience by delivering or withholding hedonistically matched appetitive and aversive tastes, and measure ERPs to cues signaling each taste. We observed a typical FRN (computed as the loss-minus-gain difference wave) to appetitive taste, but a reverse FRN to aversive taste. When tested axiomatically, frontocentral ERPs showed a salience response across tastes, with a particularly early response to outcome delivery, supporting recent propositions of a fast, unsigned, and unspecific response to salient stimuli. ERPs also expressed aversive prediction error peaking at 285 ms, which conformed to the logic of an axiomatic model of prediction error. With stimuli that most resemble those used in animal models, we did not detect any frontocentral ERP signal for utility prediction error, in contrast with dominant views of the functional role of the FRN ERP. We link the animal and human literature and present a challenge for current perspectives on associative learning research using ERPs.

Core dysfunction in schizophrenia: electrophysiology trait biomarkers.

OBJECTIVE: Core symptoms of schizophrenia, particularly in the cognitive domain are hypothesized to be due to an abnormality in neural connectivity. Biomarkers of connectivity may therefore be a promising tool in exploring the aetiology of schizophrenia. We used electrophysiological methods to demonstrate abnormal visual information processing during in patients performing a simple cognitive task. METHOD: Electrophysiological recordings were acquired from 20 chronically ill, medicated patients diagnosed with either schizophrenia or schizo-affective disorder and 20 healthy volunteers while they conducted a working memory (WM) task. RESULTS: The patient group had significantly lower accuracy on the WM task and a trend for slower responses. An early visual evoked response potential was reduced in patients. Analysis of the electroencephalographic oscillations showed a decreased phase-locking factor (in the theta, beta and gamma bands) and signal power (theta frequency band). The beta and gamma oscillatory abnormalities were confined to two sets of correlated fronto and occipital electrodes. CONCLUSION: The findings of event-related potential and oscillatory abnormalities in patients with schizophrenia confirm the sensitivity of early visual information processing measurements for identification of schizophrenia phenotype. The fronto-occipital distribution of the oscillatory abnormalities replicates our findings from a schizotypal sample and implicates a possible top-down dysfunction as a vulnerability trait.

Arthritic pain is processed in brain areas concerned with emotions and fear.

OBJECTIVE: Functional neuroimaging studies have shown that experimentally induced acute pain is processed within at least 2 parallel networks of brain structures collectively known as the pain matrix. The relevance of this finding to clinical pain is not known, because no direct comparisons of experimental and clinical pain have been performed in the same group of patients. The aim of this study was to compare directly the brain areas involved in processing arthritic pain and experimental pain in a group of patients with osteoarthritis (OA). METHODS: Twelve patients with knee OA underwent positron emission tomography of the brain, using (18)F-fluorodeoxyglucose (FDG). Scanning was performed during 3 different pain states: arthritic knee pain, experimental knee pain, and pain-free. Significant differences in the neuronal uptake of FDG between different pain states were investigated using statistical parametric mapping software. RESULTS: Both pain conditions activated the pain matrix, but arthritic pain was associated with increased activity in the cingulate cortex, the thalamus, and the amygdala; these areas are involved in the processing of fear, emotions, and in aversive conditioning. CONCLUSION: Our results suggest that studies of experimental pain provide a relevant but quantitatively incomplete picture of brain activity during arthritic pain. The search for new analgesics for arthritis that act on the brain should focus on drugs that modify this circuitry.

Removal of eye movement artefacts from single channel recordings of retinal evoked potentials using synchronous dynamical embedding and independent component analysis.

A system is described for the removal of eye movement and blink artefacts from single channel pattern reversal electroretinogram recordings of very poor signal-to-noise ratios. Artefacts are detected and removed by using a blind source separation technique based on the jadeR independent component analysis algorithm. The single channel data are arranged as a series of overlapping time-delayed vectors forming a dynamical embedding matrix. The structure of this matrix is constrained to the phase of the stimulation epoch: the term synchronous dynamical embedding is coined. A novel method using a marker channel with a non-independent synchronous feature is employed to identify the single most relevant source estimation for reconstruction and signal recovery. This method is non-lossy, all underlying signal being recovered. In synthetic datasets of defined noise content and in standardised real data recordings, the performance of this technique is compared to conventional fixed-threshold hard-limit rejection. The most significant relative improvements are achieved when movement and blink artefacts are greatest: no improvement is demonstrable for the random noise only situation.

Categories of placebo response in the absence of site-specific expectation of analgesia.

Experimental placebo analgesia is induced by building an expectation of reduced pain in a specific body part, usually using an inert cream in the guise of a local anaesthetic in conjunction with conditioning. We investigated non-site-specific placebo analgesia by conditioning subjects to expect the anaesthetic cream on one arm, without specifying if they will definitely receive the cream, or to which arm it might be applied. Painful heat pulses (150 ms) from a CO2 laser were delivered randomly to both arms. A treatment group (n=24) underwent three experimental blocks (pre-cream, conditioning after cream, and post-conditioning). During the conditioning block, the intensity of the stimulus was reduced on one arm only. In the post-conditioning block it was returned to the painful level. We evaluated the change of intensity rating post-conditioning compared to the pre-cream block. In contrast to a control group (n=16), the treatment group reported a significant reduction in intensity ratings (F(1,38)=12.1; p=0.001). In the treatment group, we observed a range of placebo responses: unilateral responders (33.3%), subjects with a placebo response in the conditioned arm only; bilateral responders (33.3%), subjects reporting reduction in the intensity ratings in both arms, and non-responders, whose intensity ratings were not influenced by conditioning. We discuss these responses in terms of different levels of expected analgesia, facilitated by the absence of a site-specific focus for the treatment. We suggest this allowed the individuals suggestibility to influence their assessment of the pain experience by combining different levels of expectation with the information from the actual pain stimulus.

Dipole source localisation using independent component analysis: single trial localisation of laser evoked pain.

The accuracy of the inverse solution that finds the spatial location of the generating sources from averaged scalp-recorded event related potentials (ERPs) relies on assumptions about the ERP signals and the sources. We provide evidence that using independent component analysis (ICA) as a signal decomposition filter prior to applying the inverse solution reveals sources that cannot be detected by conventional source localisation methods. Five clusters of sources emerged: a single source cluster in caudal cingulate and bilateral sources in secondary somatosensory cortex (SII), inferior parietal cortex, premotor cortex and insular cortex. The locations of the source dipoles were consistent with findings using fMRI and PET but have not all been previously detected in a single electrophysiological study. In addition, the time-course of the activation of these dipoles was estimated. The results suggest that using ICA to localise single trial data is a powerful tool for exploring the spatiotemporal dynamics of rapid and complex brain processes.

Tumour grading from magnetic resonance spectroscopy: a comparison of feature extraction with variable selection.

Magnetic resonance spectroscopy (MRS) provides a non-invasive measurement of the biochemistry of living tissue. However, signal variation due to tissue heterogeneity causes considerable mixing between different disease categories, making accurate class assignments difficult. This paper compares a systematic methodology for classifier design using multivariate bayesian variable selection (MBVS), with one based on feature extraction using independent component analysis (ICA). We illustrate the methodology and assess the classification performance using a data set comprising 41 magnetic resonance spectra acquired in vivo from two grades of brain tumour, namely low- and medium-grade astrocytic tumours, labelled astrocytomas (AST), and high-grade gliomas and glioblastomas labelled glioblastomas (GL). The aim of this study is threefold. First, to describe the application of the alternative methodologies to MRS, then to benchmark their classification performance, and finally to interpret the classification models in terms of biologically relevant signals derived from the spectra. The classification performance is assessed using the bootstrap method and by application to a test sample in a retrospective study.

Application of self-organizing maps in conformational analysis of lipids.

The characteristics of lipid assemblies are important for the functions of biological membranes. This has led to an increasing utilization of molecular dynamics simulations for the elucidation of the structural features of biomembranes. We have applied the self-organizing map (SOM) to the analysis of the complex conformational data from a 1-ns molecular dynamics simulation of PLPC phospholipids in a membrane assembly. Mapping of 1.44 million molecular conformations to a two-dimensional array of neurons revealed, without human intervention, the main conformational features in hours. Both the whole molecule and the characteristics of the unsaturated fatty acid chains were analyzed. All major structural features were easily distinguished, such as the orientational variability of the headgroup, the mainly trans state dihedral angles of the sn-1 chain, and both straight and bent conformations of the unsaturated sn-2 chain. Furthermore, presentation of the trajectory of an individual lipid molecule on the map provides information on conformational dynamics. The present results suggest that the SOM method provides a powerful tool for routinely gaining rapid insight to the main molecular conformations as well as to the conformational dynamics of any simulated molecular assembly without the requirement of a priori knowledge.

Movement discrimination by single cells in the human pallidum characterised by hidden Markov models.

In patients undergoing pallidotomy for Parkinson's disease, we recorded extracellularly from single neurons in the two internal segments (GPii, GPie) and the external segment (GPe) of the globus pallidus (GP) in response to active (cued) movements of the contralateral wrist, elbow or ankle. The patterns of cell activity occurring both before and after movement onset were analysed using hidden Markov models (HMMs) and clustered by movement type using the generative topographical mapping algorithm. Cluster separation was quantified in order to measure a cell's ability to discriminate between movements. Statistical analysis of variance indicated a significant regional gradient (GPii > GPie > GPe) of movement discrimination, while cells in all regions differentiated better between movements of different joints (wrist, elbow or ankle) than between flexion and extension of the same joint. We found that GP cells generally showed distinguishable firing patterns corresponding to more than one type of movement per cell, in support of the hypothesis that cells in these regions of the basal ganglia are not involved in preparation or execution of a single type of movement but participate in many different movements, analogous to the hidden units of a neural network. Our results also indicate that cell activity both preceding a movement and during its execution may be modelled by HMMs with only a small number of states.

Firing patterns of pallidal cells in parkinsonian patients correlate with their pre-pallidotomy clinical scores.

It is unclear how the disordered activity of cells in the basal ganglia contributes to the symptoms of Parkinson's disease (PD). We recorded from single neurons extracellularly in 3 regions of the globus pallidus (GPe, GPie and GPii) in patients undergoing pallidotomy for PD. Movement-related cell firing patterns, analysed using hidden Markov models, were significantly correlated with patients' preoperative clinical scores (off drugs). Responses of cells in GPii correlated best with the scores for specific motor tasks, rather than general ones related to activities of daily living, but the reverse was true for responses from GPe. In both GPii and GPe, a higher score (i.e. greater parkinsonian severity) was associated with greater variability in cell firing rather than an increase in firing rate itself.

Assessment of statistical and neural networks methods in NMR spectral classification and metabolite selection.

Magnetic resonance spectroscopy opens a window into the biochemistry of living tissue. However, spectra acquired from different tissue types in vivo or in vitro and from body fluids contain a large number of peaks from a range of metabolites, whose relative intensities vary substantially and in complicated ways even between successive samples from the same category. The realization of the full clinical potential of NMR spectroscopy relies, in part, on our ability to interpret and quantify the role of individual metabolites in characterizing specific tissue and tissue conditions. This paper addresses the problem of tissue classification by analysing NMR spectra using statistical and neural network methods. It assesses the performance of classification models from a range of statistical methods and compares them with the performance of artificial neural network models. The paper also assesses the consistency of the models in selecting, directly from the spectra, the subsets of metabolites most relevant for differentiating between tissue types. The analysis techniques are examined using in vitro spectra from eight classes of normal tissue and tumours obtained from rats. We show that, for the given data set, the performance of linear and non-linear methods is comparable, possibly due to the small sample size per class. We also show that using a subset of metabolites selected by linear discriminant analysis for further analysis by neural networks improves the classification accuracy, and reduces the number of metabolites necessary for correct classification.

Pretreatment prediction of the chemotherapeutic response of human glioma cell cultures using nuclear magnetic resonance spectroscopy and artificial neural networks.

Both tumor metabolism and its response to cytotoxic drugs are intrinsic properties of tumor cells. It is therefore likely that there is a relationship between the two properties, however subtle and complex, wherein the metabolic characteristics of tumor cells can reflect the inherent response (resistance or sensitivity) of these cells to cytotoxic drugs. We used artificial neural network analysis to show that it is possible to distinguish, prior to treatment, between drug-resistant and drug-sensitive human glioma cell cultures from their metabolic profiles, as given by high-resolution proton nuclear magnetic resonance spectra of the cell extracts, and to predict their cellular response to the chemotherapeutic drug 1-(2-chloroethyl)-3-cyclohexyl-1-nitrosourea in vitro. The results suggest that neural network analysis of tumor nuclear magnetic resonance spectra has potential as a prognostic tool for determining treatment of gliomas, ultimately noninvasively, and may be used to provide information about the metabolic pathways involved in drug response that may be helpful in developing novel treatments for these tumors.

Pattern recognition approaches in biomedical and clinical magnetic resonance spectroscopy: a review.

In recent years considerable effort has been devoted to applying pattern recognition techniques to the complex task of data analysis in magnetic resonance spectroscopy. It may be argued that such techniques will facilitate putting MRS technology to practical clinical use. This paper reviews approaches of pattern recognition commonly used in the analysis of MR spectra for biomedical applications. It briefly introduces the mathematical and algorithmic formulation of each of the techniques, noting their developmental background and their relationship to each other, and discusses their strengths and limitations. It then reviews how these techniques have been implemented in MRS applications. In doing so the paper also highlights a number of problems related to the design and testing of MRS/pattern recognition applications which currently prevent these techniques from being in wide practical clinical use, and suggests ways to avoid those pitfalls.