Temporal-spectral signaling of sensory information and expectations in the cerebral processing of pain.

The perception of pain is shaped by somatosensory information about threat. However, pain is also influenced by an individual's expectations. Such expectations can result in clinically relevant modulations and abnormalities of pain. In the brain, sensory information, expectations (predictions), and discrepancies thereof (prediction errors) are signaled by an extended network of brain areas which generate evoked potentials and oscillatory responses at different latencies and frequencies. However, a comprehensive picture of how evoked and oscillatory brain responses signal sensory information, predictions, and prediction errors in the processing of pain is lacking so far. Here, we therefore applied brief painful stimuli to 48 healthy human participants and independently modulated sensory information (stimulus intensity) and expectations of pain intensity while measuring brain activity using electroencephalography (EEG). Pain ratings confirmed that pain intensity was shaped by both sensory information and expectations. In contrast, Bayesian analyses revealed that stimulus-induced EEG responses at different latencies (the N1, N2, and P2 components) and frequencies (alpha, beta, and gamma oscillations) were shaped by sensory information but not by expectations. Expectations, however, shaped alpha and beta oscillations before the painful stimuli. These findings indicate that commonly analyzed EEG responses to painful stimuli are more involved in signaling sensory information than in signaling expectations or mismatches of sensory information and expectations. Moreover, they indicate that the effects of expectations on pain are served by brain mechanisms which differ from those conveying effects of sensory information on pain.

Hippocampus mediates nocebo impairment of opioid analgesia through changes in functional connectivity.

The neural mechanisms underlying placebo analgesia have attracted considerable attention over the recent years. In contrast, little is known about the neural underpinnings of a nocebo-induced increase in pain. We previously showed that nocebo-induced hyperalgesia is accompanied by increased activity in the hippocampus that scaled with the perceived level of anxiety. As a key node of the neural circuitry of perceived threat and fear, the hippocampus has recently been proposed to coordinate defensive behaviour in a context-dependent manner. Such a role requires close interactions with other regions involved in the detection of and responses to threat. Here, we investigated the functional connectivity of the hippocampus during nocebo-induced hyperalgesia. Our results show an increase in functional connectivity between hippocampus and brain regions implicated in the processing of sensory-discriminative aspects of pain (posterior insula and primary somatosensory/motor cortex) as well as the periaqueductal grey. This nocebo-induced increase in connectivity scaled with an individual's increase in anxiety. Moreover, hippocampus connectivity with the amygdala was negatively correlated with the pain intensity reported during nocebo hyperalgesia relative to the placebo condition. Our findings suggest that the hippocampus links nocebo-induced anxiety to a heightened responsiveness to nociceptive input through changes in its crosstalk with pain-modulatory brain areas.

Fatigue, depression, and pain in multiple sclerosis: How neuroinflammation translates into dysfunctional reward processing and anhedonic symptoms.

Fatigue, depression, and pain affect the majority of multiple sclerosis (MS) patients, which causes a substantial burden to patients and society. The pathophysiology of these symptoms is not entirely clear, and current treatments are only partially effective. Clinically, these symptoms share signs of anhedonia, such as reduced motivation and a lack of positive affect. In the brain, they are associated with overlapping structural and functional alterations in areas involved in reward processing. Moreover, neuroinflammation has been shown to directly impede monoaminergic neurotransmission that plays a key role in reward processing. Here, we review recent neuroimaging and neuroimmunological findings, which indicate that dysfunctional reward processing might represent a shared functional mechanism fostering the symptom cluster of fatigue, depression, and pain in MS. We propose a framework that integrates these findings with a focus on monoaminergic neurotransmission and discuss its therapeutic implications, limitations, and perspectives.

Neural oscillations and connectivity characterizing the state of tonic experimental pain in humans.

Pain is a complex phenomenon that is served by neural oscillations and connectivity involving different brain areas and frequencies. Here, we aimed to systematically and comprehensively assess the pattern of neural oscillations and connectivity characterizing the state of tonic experimental pain in humans. To this end, we applied 10-min heat pain stimuli consecutively to the right and left hand of 39 healthy participants and recorded electroencephalography. We systematically analyzed global and local measures of oscillatory brain activity, connectivity, and graph theory-based network measures during tonic pain and compared them to a nonpainful control condition. Local measures showed suppressions of oscillatory activity at alpha frequencies together with stronger connectivity at alpha and beta frequencies in sensorimotor areas during tonic pain. Furthermore, sensorimotor areas contralateral to stimulation showed significantly increased connectivity to a common area in the medial prefrontal cortex at alpha frequencies. Together, these observations indicate that the state of tonic experimental pain is associated with a sensorimotor-prefrontal network connected at alpha frequencies. These findings represent a step further toward understanding the brain mechanisms underlying long-lasting pain states in health and disease.

Prefrontal gamma oscillations reflect ongoing pain intensity in chronic back pain patients.

Chronic pain is a major health care issue characterized by ongoing pain and a variety of sensory, cognitive, and affective abnormalities. The neural basis of chronic pain is still not completely understood. Previous work has implicated prefrontal brain areas in chronic pain. Furthermore, prefrontal neuronal oscillations at gamma frequencies (60-90 Hz) have been shown to reflect the perceived intensity of longer lasting experimental pain in healthy human participants. In contrast, noxious stimulus intensity has been related to alpha (8-13 Hz) and beta (14-29 Hz) oscillations in sensorimotor areas. However, it is not fully understood how the intensity of ongoing pain as the key symptom of chronic pain is represented in the human brain. Here, we asked 31 chronic back pain patients to continuously rate their ongoing pain while simultaneously recording electroencephalography (EEG). Time-frequency analyses revealed a positive association between ongoing pain intensity and prefrontal beta and gamma oscillations. No association was found between pain and alpha or beta oscillations in sensorimotor areas. These findings indicate that ongoing pain as the key symptom of chronic pain is reflected by neuronal oscillations implicated in the subjective perception of longer lasting pain rather than by neuronal oscillations related to the processing of objective nociceptive input. The findings, thus, support a dissociation of pain intensity from nociceptive processing in chronic back pain patients. Furthermore, although possible confounds by muscle activity have to be taken into account, they might be useful for defining a neurophysiological marker of ongoing pain in the human brain.

Metabolic connectivity mapping reveals effective connectivity in the resting human brain.

Directionality of signaling among brain regions provides essential information about human cognition and disease states. Assessing such effective connectivity (EC) across brain states using functional magnetic resonance imaging (fMRI) alone has proven difficult, however. We propose a novel measure of EC, termed metabolic connectivity mapping (MCM), that integrates undirected functional connectivity (FC) with local energy metabolism from fMRI and positron emission tomography (PET) data acquired simultaneously. This method is based on the concept that most energy required for neuronal communication is consumed postsynaptically, i.e., at the target neurons. We investigated MCM and possible changes in EC within the physiological range using "eyes open" versus "eyes closed" conditions in healthy subjects. Independent of condition, MCM reliably detected stable and bidirectional communication between early and higher visual regions. Moreover, we found stable top-down signaling from a frontoparietal network including frontal eye fields. In contrast, we found additional top-down signaling from all major clusters of the salience network to early visual cortex only in the eyes open condition. MCM revealed consistent bidirectional and unidirectional signaling across the entire cortex, along with prominent changes in network interactions across two simple brain states. We propose MCM as a novel approach for inferring EC from neuronal energy metabolism that is ideally suited to study signaling hierarchies in the brain and their defects in brain disorders.

Brain oscillations differentially encode noxious stimulus intensity and pain intensity.

Noxious stimuli induce physiological processes which commonly translate into pain. However, under certain conditions, pain intensity can substantially dissociate from stimulus intensity, e.g. during longer-lasting pain in chronic pain syndromes. How stimulus intensity and pain intensity are differentially represented in the human brain is, however, not yet fully understood. We therefore used electroencephalography (EEG) to investigate the cerebral representation of noxious stimulus intensity and pain intensity during 10min of painful heat stimulation in 39 healthy human participants. Time courses of objective stimulus intensity and subjective pain ratings indicated a dissociation of both measures. EEG data showed that stimulus intensity was encoded by decreases of neuronal oscillations at alpha and beta frequencies in sensorimotor areas. In contrast, pain intensity was encoded by gamma oscillations in the medial prefrontal cortex. Contrasting right versus left hand stimulation revealed that the encoding of stimulus intensity in contralateral sensorimotor areas depended on the stimulation side. In contrast, a conjunction analysis of right and left hand stimulation revealed that the encoding of pain in the medial prefrontal cortex was independent of the side of stimulation. Thus, the translation of noxious stimulus intensity into pain is associated with a change from a spatially specific representation of stimulus intensity by alpha and beta oscillations in sensorimotor areas to a spatially independent representation of pain by gamma oscillations in brain areas related to cognitive and affective-motivational processes. These findings extend the understanding of the brain mechanisms of nociception and pain and their dissociations during longer-lasting pain as a key symptom of chronic pain syndromes.

Influence of pain on motor preparation in the human brain.

The protective function of pain depends on appropriate motor responses to avoid injury and promote recovery. The preparation and execution of motor responses is thus an essential part of pain. However, it is not yet fully understood how pain and motor processes interact in the brain. Here we used electroencephalography to investigate the effects of pain on motor preparation in the human brain. Twenty healthy human participants performed a motor task in which they performed button presses to stop increasingly painful thermal stimuli when they became intolerable. In another condition, participants performed button presses without concurrent stimulation. The results show that the amplitudes of preparatory event-related desynchronizations at alpha and beta frequencies did not differ between conditions. In contrast, the amplitude of the preparatory readiness potential was reduced when a button press was performed to stop a painful stimulus compared with a button press without concomitant pain. A control experiment with nonpainful thermal stimuli showed a similar reduction of the readiness potential when a button press was performed to stop a nonpainful thermal stimulus. Together, these findings indicate that painful and nonpainful thermal stimuli can similarly influence motor preparation in the human brain. Pain-specific effects on motor preparation in the human brain remain to be demonstrated.NEW & NOTEWORTHY Pain is inherently linked to motor processes, but the interactions between pain and motor processes in the human brain are not yet fully understood. Using electroencephalography, we show that pain reduces movement-preparatory brain activity. Further results indicate that this effect is not pain specific but independent of the modality of stimulation.

Prevalence of neuropathic pain in early multiple sclerosis.

BACKGROUND: Pain is considered a frequent symptom in multiple sclerosis. Neuropathic pain is the type of pain most closely related to the pathology of multiple sclerosis and its prevalence estimates vary largely. OBJECTIVE: We prospectively assessed the prevalence of neuropathic pain in patients with early multiple sclerosis and investigated the association of neuropathic pain with other clinical parameters. METHODS: A total of 377 outpatients with multiple sclerosis at an early disease stage were included in this prospective study. Mean disease duration was 4.2 years, mean Expanded Disability Status Scale (EDSS) score was 1.6, 96.8% of patients were classified as having relapsing-remitting multiple sclerosis. Neuropathic pain was assessed using the PainDETECT questionnaire (PDQ). Depression, fatigue and cognition were assessed using the Beck Depression Inventory (BDI), the Fatigue Scale for Motor and Cognitive Functions (FSMC) and the Paced Auditory Serial Addition Test. RESULTS: PDQ scores indicative of neuropathic pain were found in 4.2% of patients. Regression analysis revealed EDSS, BDI and FMSC scores as strongest predictors of PDQ scores. CONCLUSIONS: Neuropathic pain appears to be less frequent in early multiple sclerosis than expected and is significantly associated with disability, depression and fatigue. The assessment and therapy of pain in multiple sclerosis should thus take into account neuropsychiatric symptoms already at early disease stages.

Prefrontal Gamma Oscillations Encode Tonic Pain in Humans.

Under physiological conditions, momentary pain serves vital protective functions. Ongoing pain in chronic pain states, on the other hand, is a pathological condition that causes widespread suffering and whose treatment remains unsatisfactory. The brain mechanisms of ongoing pain are largely unknown. In this study, we applied tonic painful heat stimuli of varying degree to healthy human subjects, obtained continuous pain ratings, and recorded electroencephalograms to relate ongoing pain to brain activity. Our results reveal that the subjective perception of tonic pain is selectively encoded by gamma oscillations in the medial prefrontal cortex. We further observed that the encoding of subjective pain intensity experienced by the participants differs fundamentally from that of objective stimulus intensity and from that of brief pain stimuli. These observations point to a role for gamma oscillations in the medial prefrontal cortex in ongoing, tonic pain and thereby extend current concepts of the brain mechanisms of pain to the clinically relevant state of ongoing pain. Furthermore, our approach might help to identify a brain marker of ongoing pain, which may prove useful for the diagnosis and therapy of chronic pain.

Decoding an individual's sensitivity to pain from the multivariate analysis of EEG data.

The perception of pain is characterized by its tremendous intra- and interindividual variability. Different individuals perceive the very same painful event largely differently. Here, we aimed to predict the individual pain sensitivity from brain activity. We repeatedly applied identical painful stimuli to healthy human subjects and recorded brain activity by using electroencephalography (EEG). We applied a multivariate pattern analysis to the time-frequency transformed single-trial EEG responses. Our results show that a classifier trained on a group of healthy individuals can predict another individual's pain sensitivity with an accuracy of 83%. Classification accuracy depended on pain-evoked responses at about 8 Hz and pain-induced gamma oscillations at about 80 Hz. These results reveal that the temporal-spectral pattern of pain-related neuronal responses provides valuable information about the perception of pain. Beyond, our approach may help to establish an objective neuronal marker of pain sensitivity which can potentially be recorded from a single EEG electrode.

Prestimulus functional connectivity determines pain perception in humans.

Pain is a highly subjective experience that can be substantially influenced by differences in individual susceptibility as well as personality. How susceptibility to pain and personality translate to brain activity is largely unknown. Here, we report that the functional connectivity of two key brain areas before a sensory event reflects the susceptibility to a subsequent noxious stimulus being perceived as painful. Specifically, the prestimulus connectivity among brain areas related to the subjective perception of the body and to the modulation of pain (anterior insular cortex and brainstem, respectively) determines whether a noxious event is perceived as painful. Further, these effects of prestimulus connectivity on pain perception covary with pain-relevant personality traits. More anxious and pain-attentive individuals display weaker descending connectivity to pain modulatory brain areas. We conclude that variations in functional connectivity underlie personality-related differences in individual susceptibility to pain.

Resting-state electroencephalography and magnetoencephalography as biomarkers of chronic pain: a systematic review.

ABSTRACT: Reliable and objective biomarkers promise to improve the assessment and treatment of chronic pain. Resting-state electroencephalography (EEG) is broadly available, easy to use, and cost efficient and, therefore, appealing as a potential biomarker of chronic pain. However, results of EEG studies are heterogeneous. Therefore, we conducted a systematic review (PROSPERO CRD42021272622) of quantitative resting-state EEG and magnetoencephalography (MEG) studies in adult patients with different types of chronic pain. We excluded populations with severe psychiatric or neurologic comorbidity. Risk of bias was assessed using a modified Newcastle-Ottawa Scale. Semiquantitative data synthesis was conducted using modified albatross plots. We included 76 studies after searching MEDLINE, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and EMBASE. For cross-sectional studies that can serve to develop diagnostic biomarkers, we found higher theta and beta power in patients with chronic pain than in healthy participants. For longitudinal studies, which can yield monitoring and/or predictive biomarkers, we found no clear associations of pain relief with M/EEG measures. Similarly, descriptive studies that can yield diagnostic or monitoring biomarkers showed no clear correlations of pain intensity with M/EEG measures. Risk of bias was high in many studies and domains. Together, this systematic review synthesizes evidence on how resting-state M/EEG might serve as a diagnostic biomarker of chronic pain. Beyond, this review might help to guide future M/EEG studies on the development of pain biomarkers.

Resting-state EEG and MEG biomarkers of pathological fatigue - A transdiagnostic systematic review.

Fatigue is a highly prevalent and disabling symptom of many disorders and syndromes, resulting from different pathomechanisms. However, whether and how different mechanisms converge and result in similar symptomatology is only partially understood, and transdiagnostic biomarkers that could further the diagnosis and treatment of fatigue are lacking. We, therefore, performed a transdiagnostic systematic review (PROSPERO: CRD42022330113) of quantitative resting-state electroencephalography (EEG) and magnetoencephalography (MEG) studies in adult patients suffering from pathological fatigue in different disorders. Studies investigating fatigue in healthy participants were excluded. The risk of bias was assessed using a modified Newcastle-Ottawa Scale. Semi-quantitative data synthesis was conducted using modified albatross plots. After searching MEDLINE, Web of Science Core Collection, and EMBASE, 26 studies were included. Cross-sectional studies revealed increased brain activity at theta frequencies and decreased activity at alpha frequencies as potential diagnostic biomarkers. However, the risk of bias was high in many studies and domains. Together, this transdiagnostic systematic review synthesizes evidence on how resting-state M/EEG might serve as a diagnostic biomarker of pathological fatigue. Beyond, this review might help to guide future M/EEG studies on the development of fatigue biomarkers.

DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience.

Biomarker discovery in neurological and psychiatric disorders critically depends on reproducible and transparent methods applied to large-scale datasets. Electroencephalography (EEG) is a promising tool for identifying biomarkers. However, recording, preprocessing, and analysis of EEG data is time-consuming and researcher-dependent. Therefore, we developed DISCOVER-EEG, an open and fully automated pipeline that enables easy and fast preprocessing, analysis, and visualization of resting state EEG data. Data in the Brain Imaging Data Structure (BIDS) standard are automatically preprocessed, and physiologically meaningful features of brain function (including oscillatory power, connectivity, and network characteristics) are extracted and visualized using two open-source and widely used Matlab toolboxes (EEGLAB and FieldTrip). We tested the pipeline in two large, openly available datasets containing EEG recordings of healthy participants and patients with a psychiatric condition. Additionally, we performed an exploratory analysis that could inspire the development of biomarkers for healthy aging. Thus, the DISCOVER-EEG pipeline facilitates the aggregation, reuse, and analysis of large EEG datasets, promoting open and reproducible research on brain function.

Local brain oscillations and interregional connectivity differentially serve sensory and expectation effects on pain.

Pain emerges from the integration of sensory information about threats and contextual information such as an individual's expectations. However, how sensory and contextual effects on pain are served by the brain is not fully understood so far. To address this question, we applied brief painful stimuli to 40 healthy human participants and independently varied stimulus intensity and expectations. Concurrently, we recorded electroencephalography. We assessed local oscillatory brain activity and interregional functional connectivity in a network of six brain regions playing key roles in the processing of pain. We found that sensory information predominantly influenced local brain oscillations. In contrast, expectations exclusively influenced interregional connectivity. Specifically, expectations altered connectivity at alpha (8 to 12 hertz) frequencies from prefrontal to somatosensory cortex. Moreover, discrepancies between sensory information and expectations, i.e., prediction errors, influenced connectivity at gamma (60 to 100 hertz) frequencies. These findings reveal how fundamentally different brain mechanisms serve sensory and contextual effects on pain.

Decoding the perception of pain from fMRI using multivariate pattern analysis.

Pain is known to comprise sensory, cognitive, and affective aspects. Despite numerous previous fMRI studies, however, it remains open which spatial distribution of activity is sufficient to encode whether a stimulus is perceived as painful or not. In this study, we analyzed fMRI data from a perceptual decision-making task in which participants were exposed to near-threshold laser pulses. Using multivariate analyses on different spatial scales, we investigated the predictive capacity of fMRI data for decoding whether a stimulus had been perceived as painful. Our analysis yielded a rank order of brain regions: during pain anticipation, activity in the periaqueductal gray (PAG) and orbitofrontal cortex (OFC) afforded the most accurate trial-by-trial discrimination between painful and non-painful experiences; whereas during the actual stimulation, primary and secondary somatosensory cortex, anterior insula, dorsolateral and ventrolateral prefrontal cortex, and OFC were most discriminative. The most accurate prediction of pain perception from the stimulation period, however, was enabled by the combined activity in pain regions commonly referred to as the 'pain matrix'. Our results demonstrate that the neural representation of (near-threshold) pain is spatially distributed and can be best described at an intermediate spatial scale. In addition to its utility in establishing structure-function mappings, our approach affords trial-by-trial predictions and thus represents a step towards the goal of establishing an objective neuronal marker of pain perception.

γ Oscillations are involved in the sensorimotor transformation of pain.

Pain signals threat and initiates motor responses to avoid harm. The transformation of pain into a motor response is thus an essential part of pain. Here, we investigated the neural mechanisms subserving the sensorimotor transformation of pain at the cortical level by using electroencephalography. In a simple reaction time experiment, brief painful stimuli were delivered to the left hand of healthy human subjects who responded with button presses of the right hand. The results show that the simple reaction time task was associated with neuronal responses at delta/theta, alpha/beta, and gamma frequencies. The analysis of the relationship between neuronal activity and response speed revealed that gamma oscillations, which were temporally coupled to the painful stimuli, but not temporally coupled to the motor response, predicted reaction times. Lateralization of gamma oscillations indicates that they originate from motor areas rather than from sensory areas. We conclude that gamma oscillations are involved in the sensorimotor transformation of pain whose efficiency they reflect. We hypothesize that the relationship between stimulus-locked gamma oscillations and reaction times reflects a direct thalamo-motor route of nociceptive information that is central to the biological function of pain.

Flexible cerebral connectivity patterns subserve contextual modulations of pain.

The perception of pain can be significantly modulated by the behavioral context. Here, we investigated how contextual modulations of pain are subserved in the human brain. We independently modulated the attentional and emotional context of painful stimuli and recorded brain activity by using functional magnetic resonance imaging. Our results confirm that attention to pain and a negative emotional context increases pain perception and this is concomitantly associated with increased neural activity in the anterior insular cortex. Connectivity analyses further reveal that during attentional and emotional modulations of pain, the anterior insula selectively and flexibly connects to attentional and emotional brain networks in frontoparietal and medial temporal lobe areas, respectively. We conclude that the flexible functional connectivity of the anterior insula to other functional systems of the brain, for example, attentional and emotional brain networks, subserves the extraordinary sensitivity of the pain experience to contextual modulations.

Neurophysiological coding of traits and states in the perception of pain.

Perception is not a simple reflection of sensory information but varies within and between individuals. This applies particularly to the perception of pain, which, in the brain, is associated with neuronal responses at different frequencies. Here, we show how these different neuronal responses subserve interindividual and intraindividual variations in the perception of identical painful stimuli. A time-frequency analysis of single trial electroencephalographic data indicates that pain-related responses in the theta frequency range but not at higher gamma frequencies code for interindividual variations in the perception of pain. In contrast, both pain-related theta and gamma responses provide different and complementary information on intraindividual variations in the pain experience. We conclude that theta responses reflect rather constant physiological and psychological traits of the individual, whereas gamma responses relate to short-term modulations of the individual's state. These findings reveal how neuronal responses at different frequencies differentially contribute to the translation of sensory information into a subjective percept.