The effect of age on alpha rhythms in the human brain derived from source localized resting-state electroencephalography.

With increasing age, peak alpha frequency (PAF) is slowed, and alpha power is reduced during resting-states with eyes closed. These age-related changes are evident across the whole scalp but remained unclear at the source level. The purpose of this study was to determine whether age impacts the power and frequency of the dominant alpha rhythm equally across source generators or whether the impact of age varies across sources. A total of 28 young adults and 26 elderly adults were recruited. High-density EEG was recorded for 10 mins with eyes closed. Single dipoles for each independent component were localized and clustered based on their anatomical label, resulting in 36 clusters. Meta-analyses were then conducted to assess effect sizes for PAF and power at PAF for all 36 clusters. Subgroup analyses were then implemented for frontal, sensorimotor, parietal, temporal, and occipital regions. The results of the meta-analyses showed that the elderly group exhibited slower PAF and less power at PAF compared to the young group. Subgroup analyses revealed age effects on PAF in parietal (g = 0.38), temporal (g = 0.65), and occipital regions (g = 1.04), with the largest effects observed in occipital regions. For power at PAF, age effects were observed in sensorimotor (g = 0.84) and parietal regions (g = 0.80), with the sensorimotor region showing the largest effect. Our findings show that age-related slowing and attenuation of the alpha rhythm manifests differentially across cortical regions, with sensorimotor and occipital regions most susceptible to age effects.

Diffusion MRI relates to plasma Aß42/40 in PET negative participants without dementia.

INTRODUCTION: Magnetic resonance imaging (MRI) biomarkers are needed for indexing early biological stages of Alzheimer's disease (AD), such as plasma amyloid-ß (Aß42/40) positivity in Aß positron emission tomography (PET) negative individuals. METHODS: Diffusion free-water (FW) MRI was acquired in individuals with normal cognition (NC) and mild cognitive impairment (MCI) with Aß plasma-/PET- (NC = 22, MCI = 60), plasma+/PET- (NC = 5, MCI = 20), and plasma+/PET+ (AD dementia = 21) biomarker status. Gray and white matter FW and fractional anisotropy (FAt) were compared cross-sectionally and the relationships between imaging, plasma and PET biomarkers were assessed. RESULTS: Plasma+/PET- demonstrated increased FW (24 regions) and decreased FAt (66 regions) compared to plasma-/PET-. FW (16 regions) and FAt (51 regions) were increased in plasma+/PET+ compared to plasma+/PET-. Composite brain FW correlated with plasma Aß42/40 and p-tau181. DISCUSSION: FW imaging changes distinguish plasma Aß42/40 positive and negative groups, independent of group differences in cognitive status, Aß PET status, and other plasma biomarkers (i.e., t-tau, p-tau181, glial fibrillary acidic protein, neurofilament light). HIGHLIGHTS: Plasma Aß42/40 positivity is associated with brain microstructure decline. Plasma+/PET- demonstrated increased FW in 24 total GM and WM regions. Plasma+/PET- demonstrated decreased FAt in 66 total GM and WM regions. Whole-brain FW correlated with plasma Aß42/40 and p-tau181 measures. Plasma+/PET- demonstrated decreased cortical volume and thickness.

Visuomotor brain network activation and functional connectivity among individuals with autism spectrum disorder.

Sensorimotor abnormalities are common in autism spectrum disorder (ASD) and predictive of functional outcomes, though their neural underpinnings remain poorly understood. Using functional magnetic resonance imaging, we examined both brain activation and functional connectivity during visuomotor behavior in 27 individuals with ASD and 30 typically developing (TD) controls (ages 9-35 years). Participants maintained a constant grip force while receiving visual feedback at three different visual gain levels. Relative to controls, ASD participants showed increased force variability, especially at high gain, and reduced entropy. Brain activation was greater in individuals with ASD than controls in supplementary motor area, bilateral superior parietal lobules, and contralateral middle frontal gyrus at high gain. During motor action, functional connectivity was reduced between parietal-premotor and parietal-putamen in individuals with ASD compared to controls. Individuals with ASD also showed greater age-associated increases in functional connectivity between cerebellum and visual, motor, and prefrontal cortical areas relative to controls. These results indicate that visuomotor deficits in ASD are associated with atypical activation and functional connectivity of posterior parietal, premotor, and striatal circuits involved in translating sensory feedback information into precision motor behaviors, and that functional connectivity of cerebellar-cortical sensorimotor and nonsensorimotor networks show delayed maturation.

Mean-Field Models for EEG/MEG: From Oscillations to Waves.

Neural mass models have been used since the 1970s to model the coarse-grained activity of large populations of neurons. They have proven especially fruitful for understanding brain rhythms. However, although motivated by neurobiological considerations they are phenomenological in nature, and cannot hope to recreate some of the rich repertoire of responses seen in real neuronal tissue. Here we consider a simple spiking neuron network model that has recently been shown to admit an exact mean-field description for both synaptic and gap-junction interactions. The mean-field model takes a similar form to a standard neural mass model, with an additional dynamical equation to describe the evolution of within-population synchrony. As well as reviewing the origins of this next generation mass model we discuss its extension to describe an idealised spatially extended planar cortex. To emphasise the usefulness of this model for EEG/MEG modelling we show how it can be used to uncover the role of local gap-junction coupling in shaping large scale synaptic waves.

Cortical dynamics of movement-evoked pain in chronic low back pain.

KEY POINTS: Cortical activity underlying movement-evoked pain is not well understood, despite being a key symptom of chronic musculoskeletal pain. We combined high-density electroencephalography with a full-body reaching protocol in a virtual reality environment to assess cortical activity during movement-evoked pain in chronic low back pain. Movement-evoked pain in individuals with chronic low back pain was associated with longer reaction times, delayed peak velocity and greater movement variability. Movement-evoked pain was associated with attenuated disinhibition in prefrontal motor areas, as evidenced by an attenuated reduction in beta power in the premotor cortex and supplementary motor area. ABSTRACT: Although experimental pain alters neural activity in the cortex, evidence of changes in neural activity in individuals with chronic low back pain (cLBP) remains scarce and results are inconsistent. One of the challenges in studying cLBP is that the clinical pain fluctuates over time and often changes during movement. The goal of the present study was to address this challenge by recording high-density electroencephalography (HD-EEG) data during a full-body reaching task to understand neural activity during movement-evoked pain. HD-EEG data were analysed using independent component analyses, source localization and measure projection analyses to compare neural oscillations between individuals with cLBP who experienced movement-evoked pain and pain-free controls. We report two novel findings. First, movement-evoked pain in individuals with cLBP was associated with longer reaction times, delayed peak velocity and greater movement variability. Second, movement-evoked pain was associated with an attenuated reduction in beta power in the premotor cortex and supplementary motor area. Our observations move the field forward by revealing attenuated disinhibition in prefrontal motor areas during movement-evoked pain in cLBP.

Unraveling somatotopic organization in the human brain using machine learning and adaptive supervoxel-based parcellations.

In addition to the well-established somatotopy in the pre- and post-central gyrus, there is now strong evidence that somatotopic organization is evident across other regions in the sensorimotor network. This raises several experimental questions: To what extent is activity in the sensorimotor network effector-dependent and effector-independent? How important is the sensorimotor cortex when predicting the motor effector? Is there redundancy in the distributed somatotopically organized network such that removing one region has little impact on classification accuracy? To answer these questions, we developed a novel experimental approach. fMRI data were collected while human subjects performed a precisely controlled force generation task separately with their hand, foot, and mouth. We used a simple linear iterative clustering (SLIC) algorithm to segment whole-brain beta coefficient maps to build an adaptive brain parcellation and then classified effectors using extreme gradient boosting (XGBoost) based on parcellations at various spatial resolutions. This allowed us to understand how data-driven adaptive brain parcellation granularity altered classification accuracy. Results revealed effector-dependent activity in regions of the post-central gyrus, precentral gyrus, and paracentral lobule. SMA, regions of the inferior and superior parietal lobule, and cerebellum each contained effector-dependent and effector-independent representations. Machine learning analyses showed that increasing the spatial resolution of the data-driven model increased classification accuracy, which reached 94% with 1755 supervoxels. Our SLIC-based supervoxel parcellation outperformed classification analyses using established brain templates and random simulations. Occlusion experiments further demonstrated redundancy across the sensorimotor network when classifying effectors. Our observations extend our understanding of effector-dependent and effector-independent organization within the human brain and provide new insight into the functional neuroanatomy required to predict the motor effector used in a motor control task.

Functional imaging of the brainstem during visually-guided motor control reveals visuomotor regions in the pons and midbrain.

Integrating visual information for motor output is an essential process of visually-guided motor control. The brainstem is known to be a major center involved in the integration of sensory information for motor output, however, limitations of functional imaging in humans have impaired our knowledge about the individual roles of sub-nuclei within the brainstem. Thus, the bulk of our knowledge surrounding the function of the brainstem is based on anatomical and behavioral studies in non-human primates, cats, and rodents, despite studies demonstrating differences in the organization of visuomotor processing between mammals. fMRI studies in humans have examined activity related to visually-guided motor tasks, however, few have done so while controlling for both force without visual feedback activity and visual stimuli without force activity. Of the studies that have controlled for both conditions, none have reported brainstem activity. Here, we employed a novel fMRI paradigm focused on the brainstem and cerebellum to systematically investigate the hypothesis that the pons and midbrain are critical for the integration of visual information for motor control. Visuomotor activity during visually-guided pinch-grip force was measured while controlling for force without visual feedback activity and visual stimuli without force activity in healthy adults. Using physiological noise correction and multiple task repetitions, we demonstrated that visuomotor activity occurs in the inferior portion of the basilar pons and the midbrain. These findings provide direct evidence in humans that the pons and midbrain support the integration of visual information for motor control. We also determined the effect of physiological noise and task repetitions on the visuomotor signal that will be useful in future studies of neurodegenerative diseases affecting the brainstem.

A dynamic neural field model of continuous input integration.

The ability of neural systems to turn transient inputs into persistent changes in activity is thought to be a fundamental requirement for higher cognitive functions. In continuous attractor networks frequently used to model working memory or decision making tasks, the persistent activity settles to a stable pattern with the stereotyped shape of a "bump" independent of integration time or input strength. Here, we investigate a new bump attractor model in which the bump width and amplitude not only reflect qualitative and quantitative characteristics of a preceding input but also the continuous integration of evidence over longer timescales. The model is formalized by two coupled dynamic field equations of Amari-type which combine recurrent interactions mediated by a Mexican-hat connectivity with local feedback mechanisms that balance excitation and inhibition. We analyze the existence, stability and bifurcation structure of single and multi-bump solutions and discuss the relevance of their input dependence to modeling cognitive functions. We then systematically compare the pattern formation process of the two-field model with the classical Amari model. The results reveal that the balanced local feedback mechanisms facilitate the encoding and maintenance of multi-item memories. The existence of stable subthreshold bumps suggests that different to the Amari model, the suppression effect of neighboring bumps in the range of lateral competition may not lead to a complete loss of information. Moreover, bumps with larger amplitude are less vulnerable to noise-induced drifts and distance-dependent interaction effects resulting in more faithful memory representations over time.

Next-generation neural mass and field modeling.

The Wilson-Cowan population model of neural activity has greatly influenced our understanding of the mechanisms for the generation of brain rhythms and the emergence of structured brain activity. As well as the many insights that have been obtained from its mathematical analysis, it is now widely used in the computational neuroscience community for building large-scale in silico brain networks that can incorporate the increasing amount of knowledge from the Human Connectome Project. Here, we consider a neural population model in the spirit of that originally developed by Wilson and Cowan, albeit with the added advantage that it can account for the phenomena of event-related synchronization and desynchronization. This derived mean-field model provides a dynamic description for the evolution of synchrony, as measured by the Kuramoto order parameter, in a large population of quadratic integrate-and-fire model neurons. As in the original Wilson-Cowan framework, the population firing rate is at the heart of our new model; however, in a significant departure from the sigmoidal firing rate function approach, the population firing rate is now obtained as a real-valued function of the complex-valued population synchrony measure. To highlight the usefulness of this next-generation Wilson-Cowan style model, we deploy it in a number of neurobiological contexts, providing understanding of the changes in power spectra observed in electro- and magnetoencephalography neuroimaging studies of motor cortex during movement, insights into patterns of functional connectivity observed during rest and their disruption by transcranial magnetic stimulation, and to describe wave propagation across cortex.

Relationships Between Neuronal Oscillatory Amplitude and Dynamic Functional Connectivity.

Event-related fluctuations of neural oscillatory amplitude are reported widely in the context of cognitive processing and are typically interpreted as a marker of brain "activity". However, the precise nature of these effects remains unclear; in particular, whether such fluctuations reflect local dynamics, integration between regions, or both, is unknown. Here, using magnetoencephalography, we show that movement induced oscillatory modulation is associated with transient connectivity between sensorimotor regions. Further, in resting-state data, we demonstrate a significant association between oscillatory modulation and dynamic connectivity. A confound with such empirical measurements is that increased amplitude necessarily means increased signal-to-noise ratio (SNR): this means that the question of whether amplitude and connectivity are genuinely coupled, or whether increased connectivity is observed purely due to increased SNR is unanswered. Here, we counter this problem by analogy with computational models which show that, in the presence of global network coupling and local multistability, the link between oscillatory modulation and long-range connectivity is a natural consequence of neural networks. Our results provide evidence for the notion that connectivity is mediated by neural oscillations, and suggest that time-frequency spectrograms are not merely a description of local synchrony but also reflect fluctuations in long-range connectivity.

Excitable dynamics in neural and cardiac systems.

The application of mathematics, physics and engineering to medical research is continuously growing; interactions among these disciplines have become increasingly important and have contributed to an improved understanding of clinical and biological phenomena, with implications for disease prevention, diagnosis and treatment. This special issue presents examples of this synergy, with a particular focus on the investigation of cardiac and neural excitability. This issue includes 24 original research papers and covers a broad range of topics related to the physiological and pathophysiological function of the brain and the heart. Studies span scales from isolated neurons and small networks of neurons to whole-organ dynamics for the brain and from cardiac subcellular domains and cardiomyocytes to one-dimensional tissues for the heart. This preface is part of the Special Issue on "Excitable Dynamics in Neural and Cardiac Systems".

Development of a transcallosal tractography template and its application to dementia.

Understanding the architecture of transcallosal connections would allow for more specific assessments of neurodegeneration across many fields of neuroscience, neurology, and psychiatry. To map these connections, we conducted probabilistic tractography in 100 Human Connectome Project subjects in 32 cortical areas using novel post-processing algorithms to create a spatially precise Trancallosal Tract Template (TCATT). We found robust transcallosal tracts in all 32 regions, and a topographical analysis in the corpus callosum largely agreed with well-established subdivisions of the corpus callosum. We then obtained diffusion MRI data from a cohort of patients with Alzheimer's disease (AD) and another with progressive supranuclear palsy (PSP) and used a two-compartment model to calculate free-water corrected fractional anisotropy (FAT) and free-water (FW) within the TCATT. These metrics were used to determine between-group differences and to determine which subset of tracts was best associated with cognitive function (Montreal Cognitive Assessment (MoCA)). In AD, we found robust between-group differences in FW (31/32 TCATT tracts) in the absence of between-group differences in FAT. FW in the inferior temporal gyrus TCATT tract was most associated with MoCA scores in AD. In PSP, there were widespread differences in both FAT and FW, and MoCA was predicted by FAT in the inferior frontal pars triangularis, preSMA, and medial frontal gyrus TCATT tracts as well as FW in the inferior frontal pars opercularis TCATT tract. The TCATT improves spatial localization of corpus callosum measurements to enhance the evaluation of treatment effects, as well as the monitoring of brain microstructure in relation to cognitive dysfunction and disease progression. Here, we have shown its direct relevance in capturing between-group differences and associating it with the MoCA in AD and PSP.

How do spatially distinct frequency specific MEG networks emerge from one underlying structural connectome? The role of the structural eigenmodes.

Functional networks obtained from magnetoencephalography (MEG) from different frequency bands show distinct spatial patterns. It remains to be elucidated how distinct spatial patterns in MEG networks emerge given a single underlying structural network. Recent work has suggested that the eigenmodes of the structural network might serve as a basis set for functional network patterns in the case of functional MRI. Here, we take this notion further in the context of frequency band specific MEG networks. We show that a selected set of eigenmodes of the structural network can predict different frequency band specific networks in the resting state, ranging from delta (1-4 Hz) to the high gamma band (40-70 Hz). These predictions outperform predictions based from surrogate data, suggesting a genuine relationship between eigenmodes of the structural network and frequency specific MEG networks. We then show that the relevant set of eigenmodes can be excited in a network of neural mass models using linear stability analysis only by including delays. Excitation of an eigenmode in this context refers to a dynamic instability of a network steady state to a spatial pattern with a corresponding coherent temporal oscillation. Simulations verify the results from linear stability analysis and suggest that theta, alpha and beta band networks emerge very near to the bifurcation. The delta and gamma bands in the resting state emerges further away from the bifurcation. These results show for the first time how delayed interactions can excite the relevant set of eigenmodes that give rise to frequency specific functional connectivity patterns.

Neurite orientation dispersion and density imaging (NODDI) and free-water imaging in Parkinsonism.

Neurite orientation dispersion and density imaging (NODDI) uses a three-compartment model to probe brain tissue microstructure, whereas free-water (FW) imaging models two-compartments. It is unknown if NODDI detects more disease-specific effects related to neurodegeneration in Parkinson's disease (PD) and atypical Parkinsonism. We acquired multi- and single-shell diffusion imaging at 3 Tesla across two sites. NODDI (using multi-shell; isotropic volume [Viso]; intracellular volume [Vic]; orientation dispersion [ODI]) and FW imaging (using single-shell; FW; free-water corrected fractional anisotropy [FAt]) were compared with 44 PD, 21 multiple system atrophy Parkinsonian variant (MSAp), 26 progressive supranuclear palsy (PSP), and 24 healthy control subjects in the basal ganglia, midbrain/thalamus, cerebellum, and corpus callosum. There was elevated Viso in posterior substantia nigra across Parkinsonisms, and Viso, Vic, and ODI were altered in MSAp and PSP in the striatum, globus pallidus, midbrain, thalamus, cerebellum, and corpus callosum relative to controls. The mean effect size across regions for Viso was 0.163, ODI 0.131, Vic 0.122, FW 0.359, and FAt 0.125, with extracellular compartments having the greatest effect size. A key question addressed was if these techniques discriminate PD and atypical Parkinsonism. Both NODDI (AUC: 0.945) and FW imaging (AUC: 0.969) had high accuracy, with no significant difference between models. This study provides new evidence that NODDI and FW imaging offer similar discriminability between PD and atypical Parkinsonism, and FW had higher effect sizes for detecting Parkinsonism within regions across the basal ganglia and cerebellum.

Cortical dynamics within and between parietal and motor cortex in essential tremor.

BACKGROUND: Evidence from functional imaging in essential tremor suggests that activity within parietal and motor cortices may be associated with worsening of tremor at increased visual feedback. OBJECTIVES: Examine how cortical oscillations within these regions and the connectivity between these regions is associated with worsening of tremor in essential tremor in response to high visual feedback. METHOD: The study included 24 essential tremor participants and 17 controls. We measured cortical activity and tremor magnitude at low and high feedback conditions. Cortical activity was measured using high-density electroencephalogram and isolated using source localization. RESULTS: Changes in power across feedback in the 4-12 Hz and 12-30 Hz bands were reduced within the contralateral motor cortex of essential tremor patients compared to controls. The 12-30 Hz bidirectional connectivity between the parietal and contralateral motor cortex was decreased in essential tremor patients. Worsening of tremor from low to high visual feedback was associated with 4-12 Hz activity in contralateral motor cortex. The greatest separation between groups was found when using the difference of the contralateral motor cortex activity at high and low feedback, rather than either feedback condition alone. CONCLUSION: Our findings provide new evidence that tremor in essential tremor is associated with reduced power across feedback in the motor cortex and reduced connectivity between the parietal and motor cortices. Combined with previous work on the cerebellar-thalamo-cortical motor circuit, our findings suggest that the network level disturbances associated with essential tremor extend to the cortico-cortical pathway between the parietal cortex and motor cortex. © 2018 International Parkinson and Movement Disorder Society.

Complex patterns of subcellular cardiac alternans.

Cardiac alternans, in which the membrane potential and the intracellular calcium concentration exhibit alternating durations and peak amplitudes at consecutive beats, constitute a precursor to fatal cardiac arrhythmia such as sudden cardiac death. A crucial question therefore concerns the onset of cardiac alternans. Typically, alternans are only reported when they are fully developed. Here, we present a modelling approach to explore recently discovered microscopic alternans, which represent one of the earliest manifestations of cardiac alternans. In this case, the regular periodic dynamics of the local intracellular calcium concentration is already unstable, while the whole-cell behaviour suggests a healthy cell state. In particular, we use our model to investigate the impact of calcium diffusion in both the cytosol and the sarcoplasmic reticulum on the formation of microscopic calcium alternans. We find that for dominant cytosolic coupling, calcium alternans emerge via the traditional period doubling bifurcation. In contrast, dominant luminal coupling leads to a novel route to calcium alternans through a saddle-node bifurcation at the network level. Combining semi-analytical and computational approaches, we compute areas of stability in parameter space and find that as we cross from stable to unstable regions, the emergent patterns of the intracellular calcium concentration change abruptly in a fashion that is highly dependent upon position along the stability boundary. Our results demonstrate that microscopic calcium alternans may possess a much richer dynamical repertoire than previously thought and further strengthen the role of luminal calcium in shaping cardiac calcium dynamics.

A widespread visually-sensitive functional network relates to symptoms in essential tremor.

Essential tremor is a neurological syndrome of heterogeneous pathology and aetiology that is characterized by tremor primarily in the upper extremities. This tremor is commonly hypothesized to be driven by a single or multiple neural oscillator(s) within the cerebello-thalamo-cortical pathway. Several studies have found an association of blood-oxygen level-dependent (BOLD) signal in the cerebello-thalamo-cortical pathway with essential tremor, but there is behavioural evidence that also points to the possibility that the severity of tremor could be influenced by visual feedback. Here, we directly manipulated visual feedback during a functional MRI grip force task in patients with essential tremor and control participants, and hypothesized that an increase in visual feedback would exacerbate tremor in the 4-12 Hz range in essential tremor patients. Further, we hypothesized that this exacerbation of tremor would be associated with dysfunctional changes in BOLD signal and entropy within, and beyond, the cerebello-thalamo-cortical pathway. We found that increases in visual feedback increased tremor in the 4-12 Hz range in essential tremor patients, and this increase in tremor was associated with abnormal changes in BOLD amplitude and entropy in regions within the cerebello-thalamo-motor cortical pathway, and extended to visual and parietal areas. To determine if the tremor severity was associated with single or multiple brain region(s), we conducted a birectional stepwise multiple regression analysis, and found that a widespread functional network extending beyond the cerebello-thalamo-motor cortical pathway was associated with changes in tremor severity measured during the imaging protocol. Further, this same network was associated with clinical tremor severity measured with the Fahn, Tolosa, Marin Tremor Rating Scale, suggesting this network is clinically relevant. Since increased visual feedback also reduced force error, this network was evaluated in relation to force error but the model was not significant, indicating it is associated with force tremor but not force error. This study therefore provides new evidence that a widespread functional network is associated with the severity of tremor in patients with essential tremor measured simultaneously at the hand during functional imaging, and is also associated with the clinical severity of tremor. These findings support the idea that the severity of tremor is exacerbated by increased visual feedback, suggesting that designers of new computing technologies should consider using lower visual feedback levels to reduce tremor in essential tremor.

A Template and Probabilistic Atlas of the Human Sensorimotor Tracts using Diffusion MRI.

The purpose of this study was to develop a high-resolution sensorimotor area tract template (SMATT) which segments corticofugal tracts based on 6 cortical regions in primary motor cortex, dorsal premotor cortex, ventral premotor cortex, supplementary motor area (SMA), pre-supplementary motor area (preSMA), and primary somatosensory cortex using diffusion tensor imaging. Individual probabilistic tractography analyses were conducted in 100 subjects using the highest resolution data currently available. Tractography results were refined using a novel algorithm to objectively determine slice level thresholds that best minimized overlap between tracts while preserving tract volume. Consistent with tracing studies in monkey and rodent, our observations show that cortical topography is generally preserved through the internal capsule, with the preSMA tract remaining most anterior and the primary somatosensory tract remaining most posterior. We combine our results into a freely available white matter template named the SMATT. We also provide a probabilistic SMATT that quantifies the extent of overlap between tracts. Finally, we assess how the SMATT operates at the individual subject level in another independent data set, and in an individual after stroke. The SMATT and probabilistic SMATT provide new tools that segment and label sensorimotor tracts at a spatial resolution not previously available.

Pain-Related Suppression of Beta Oscillations Facilitates Voluntary Movement.

Increased beta oscillations over sensorimotor cortex are antikinetic. Motor- and pain-related processes separately suppress beta oscillations over sensorimotor cortex leading to the prediction that ongoing pain should facilitate movement. In the current study, we used a paradigm in which voluntary movements were executed during an ongoing pain-eliciting stimulus to test the hypothesis that a pain-related suppression of beta oscillations would facilitate the initiation of a subsequent voluntary movement. Using kinematic measures, electromyography, and high-density electroencephalography, we demonstrate that ongoing pain leads to shorter reaction times without affecting the kinematics or accuracy of movement. Reaction time was positively correlated with beta power prior to movement in contralateral premotor areas. Our findings corroborate the view that beta-band oscillations are antikinetic and provide new evidence that pain primes the motor system for action. Our observations provide the first evidence that a pain-related suppression of beta oscillations over contralateral premotor areas leads to shorter reaction times for voluntary movement.

Automated classification of pain perception using high-density electroencephalography data.

The translation of brief, millisecond-long pain-eliciting stimuli to the subjective perception of pain is associated with changes in theta, alpha, beta, and gamma oscillations over sensorimotor cortex. However, when a pain-eliciting stimulus continues for minutes, regions beyond the sensorimotor cortex, such as the prefrontal cortex, are also engaged. Abnormalities in prefrontal cortex have been associated with chronic pain states, but conventional, millisecond-long EEG paradigms do not engage prefrontal regions. In the current study, we collected high-density EEG data during an experimental paradigm in which subjects experienced a 4-s, low- or high-intensity pain-eliciting stimulus. EEG data were analyzed using independent component analyses, EEG source localization analyses, and measure projection analyses. We report three novel findings. First, an increase in pain perception was associated with an increase in gamma and theta power in a cortical region that included medial prefrontal cortex. Second, a decrease in lower beta power was associated with an increase in pain perception in a cortical region that included the contralateral sensorimotor cortex. Third, we used machine learning for automated classification of EEG data into low- and high-pain classes. Theta and gamma power in the medial prefrontal region and lower beta power in the contralateral sensorimotor region served as features for classification. We found a leave-one-out cross-validation accuracy of 89.58%. The development of biological markers for pain states continues to gain traction in the literature, and our findings provide new information that advances this body of work.NEW & NOTEWORTHY The development of a biological marker for pain continues to gain traction in literature. Our findings show that high- and low-pain perception in human subjects can be classified with 89% accuracy using high-density EEG data from prefrontal cortex and contralateral sensorimotor cortex. Our approach represents a novel neurophysiological paradigm that advances the literature on biological markers for pain.