An fMRI-based neurologic signature of physical pain

First-in-human prediction of chronic pain state using intracranial neural biomarkers

Chronic pain syndromes are often refractory to treatment and cause substantial suffering and disability. Pain severity is often measured through subjective report, while objective biomarkers that may guide diagnosis and treatment are lacking. Also, which brain activity underlies chronic pain on clinically relevant timescales, or how this relates to acute pain, remains unclear. Here four individuals with refractory neuropathic pain were implanted with chronic intracranial electrodes in the anterior cingulate cortex and orbitofrontal cortex (OFC). Participants reported pain metrics coincident with ambulatory, direct neural recordings obtained multiple times daily over months. We successfully predicted intraindividual chronic pain severity scores from neural activity with high sensitivity using machine learning methods. Chronic pain decoding relied on sustained power changes from the OFC, which tended to differ from transient patterns of activity associated with acute, evoked pain states during a task. Thus, intracranial OFC signals can be used to predict spontaneous, chronic pain state in patients.

Structural brain connectivity predicts early acute pain after mild traumatic brain injury

ABSTRACT

Mild traumatic brain injury (mTBI), is a leading cause of disability worldwide, with acute pain manifesting as one of its most debilitating symptoms. Understanding acute postinjury pain is important because it is a strong predictor of long-term outcomes. In this study, we imaged the brains of 157 patients with mTBI, following a motorized vehicle collision. We extracted white matter structural connectivity networks and used a machine learning approach to predict acute pain. Stronger white matter tracts within the sensorimotor, thalamiccortical, and default-mode systems predicted 20% of the variance in pain severity within 72 hours of the injury. This result generalized in 2 independent groups: 39 mTBI patients and 13 mTBI patients without whiplash symptoms. White matter measures collected at 6 months after the collision still predicted mTBI pain at that timepoint (n = 36). These white matter connections were associated with 2 nociceptive psychophysical outcomes tested at a remote body site-namely, conditioned pain modulation and magnitude of suprathreshold pain-and with pain sensitivity questionnaire scores. Our findings demonstrate a stable white matter network, the properties of which determine an important amount of pain experienced after acute injury, pinpointing a circuitry engaged in the transformation and amplification of nociceptive inputs to pain perception.

Pain sensitivity related to gamma oscillation of parvalbumin interneuron in primary somatosensory cortex in Dync1i1-/- mice

Cytoplasmic dynein is an important intracellular motor protein that plays an important role in neuronal growth, axonal polarity formation, dendritic differentiation, and dendritic spine development among others. The intermediate chain of dynein, encoded by Dync1i1, plays a vital role in the dynein complex. Therefore, we assessed the behavioral and related neuronal activities in mice with dync1i1 gene knockout. Neuronal activities in primary somatosensory cortex were recorded by in vivo electrophysiology and manipulated by optogenetic and chemogenetics. Nociception of mechanical, thermal, and cold pain in Dync1i1^-/- mice were impaired. The activities of parvalbumin (PV) interneurons and gamma oscillation in primary somatosensory were also impaired when exposed to mechanical nociceptive stimulation. This neuronal dysfunction was rescued by optogenetic activation of PV neurons in Dync1i1^-/- mice, and mimicked by suppressing PV neurons using chemogenetics in WT mice. Impaired pain sensations in Dync1i1^-/- mice were correlated with impaired gamma oscillations due to a loss of interneurons, especially the PV type. This genotype-driven approach revealed an association between impaired pain sensation and cytoplasmic dynein complex.

Differences in the neural basis and transcriptomic patterns in acute and persistent pain-related anxiety-like behaviors

Background

Both acute and persistent pain is associated with anxiety in clinical observations, but whether the underlying neural mechanisms differ is poorly understood.

Methods

We used formalin or complete Freund's adjuvant (CFA) to induce acute or persistent pain. Behavioral performance was assessed by the paw withdrawal threshold (PWT), open field (OF), and elevated plus maze (EPM) tests. C-Fos staining was used to identify the activated brain regions. Chemogenetic inhibition was further performed to examine the necessity of brain regions in behaviors. RNA sequencing (RNA-seq) was used to identify the transcriptomic changes.

Results

Both acute and persistent pain could lead to anxiety-like behavior in mice. The c-Fos expression indicates that the bed nucleus of the stria terminalis (BNST) is activated only in acute pain, whereas the medial prefrontal cortex (mPFC) is activated only in persistent pain. Chemogenetic manipulation reveals that the activation of the BNST excitatory neurons is required for acute pain-induced anxiety-like behaviors. In contrast, the activation of the prelimbic mPFC excitatory neurons is essential for persistent pain-induced anxiety-like behaviors. RNA-seq reveals that acute and persistent pain induces differential gene expression changes and protein-protein interaction networks in the BNST and prelimbic mPFC. The genes relevant to neuronal functions might underline the differential activation of the BNST and prelimbic mPFC in different pain models, and be involved in acute and persistent pain-related anxiety-like behaviors.

Conclusion

Distinct brain regions and gene expression patterns are involved in acute and persistent pain-related anxiety-like behaviors.

The Secondary Somatosensory Cortex Gates Mechanical and Thermal Sensitivity

The cerebral cortex is vital for the perception and processing of sensory stimuli. In the somatosensory axis, information is received by two distinct regions, the primary (S1) and secondary (S2) somatosensory cortices. Top-down circuits stemming from S1 can modulate mechanical and cooling but not heat stimuli such that circuit inhibition causes blunted mechanical and cooling perception. Using optogenetics and chemogenetics, we find that in contrast to S1, an inhibition of S2 output increases mechanical and heat, but not cooling sensitivity. Combining 2-photon anatomical reconstruction with chemogenetic inhibition of specific S2 circuits, we discover that S2 projections to the secondary motor cortex (M2) govern mechanical and thermal sensitivity without affecting motor or cognitive function. This suggests that while S2, like S1, encodes specific sensory information, that S2 operates through quite distinct neural substrates to modulate responsiveness to particular somatosensory stimuli and that somatosensory cortical encoding occurs in a largely parallel fashion.

Pain and the emotional brain: pain-related cortical processes are better reflected by affective evaluation than by cognitive evaluation

The experience of pain has been dissociated into two interwoven aspects: a sensory-discriminative aspect and an affective-motivational aspect. We aimed to explore which of the pain descriptors is more deeply rooted in the human brain. Participants were asked to evaluate applied cold pain. The majority of the trials showed distinct ratings: some were rated higher for unpleasantness and others for intensity. We compared the relationship between functional data recorded from 7 T MRI with unpleasantness and intensity ratings and revealed a stronger relationship between cortical data and unpleasantness ratings. The present study underlines the importance of the emotional-affective aspects of pain-related cortical processes in the brain. The findings corroborate previous studies showing a higher sensitivity to pain unpleasantness compared to ratings of pain intensity. For the processing of pain in healthy subjects, this effect may reflect the more direct and intuitive evaluation of emotional aspects of the pain system, which is to prevent harm and to preserve the physical integrity of the body.

Value-based neural representations predict social decision preferences

Social decision-making is omnipresent in everyday life, carrying the potential for both positive and negative consequences for the decision-maker and those closest to them. While evidence suggests that decision-makers use value-based heuristics to guide choice behavior, very little is known about how decision-makers' representations of other agents influence social choice behavior. We used multivariate pattern expression analyses on fMRI data to understand how value-based processes shape neural representations of those affected by one's social decisions and whether value-based encoding is associated with social decision preferences. We found that stronger value-based encoding of a given close other (e.g. parent) relative to a second close other (e.g. friend) was associated with a greater propensity to favor the former during subsequent social decision-making. These results are the first to our knowledge to explicitly show that value-based processes affect decision behavior via representations of close others.

Non-invasive neuromodulation of sub-regions of the human insula differentially affect pain processing and heart-rate variability

The insula is a portion of the cerebral cortex folded deep within the lateral sulcus covered by the overlying opercula of the inferior frontal lobe and superior portion of the temporal lobe. The insula has been parsed into sub-regions based upon cytoarchitectonics and structural and functional connectivity with multiple lines of evidence supporting specific roles for each of these sub-regions in pain processing and interoception. In the past, causal interrogation of the insula was only possible in patients with surgically implanted electrodes. Here, we leverage the high spatial resolution combined with the deep penetration depth of low-intensity focused ultrasound (LIFU) to non-surgically modulate either the anterior insula (AI) or posterior insula (PI) in humans for effect on subjective pain ratings, electroencephalographic (EEG) contact head evoked potentials (CHEPs) and time-frequency power as well as autonomic measures including heart-rate variability (HRV) and electrodermal response (EDR). N = 23 healthy volunteers received brief noxious heat pain stimuli to the dorsum of their right hand during continuous heart-rate, EDR and EEG recording. LIFU was delivered to either the AI (anterior short gyrus), PI (posterior longus gyrus) or under an inert sham condition time-locked to the heat stimulus. Results demonstrate that single-element 500 kHz LIFU is capable of individually targeting specific gyri of the insula. LIFU to both AI and PI similarly reduced perceived pain ratings but had differential effects on EEG activity. LIFU to PI affected earlier EEG amplitudes around 300 milliseconds whereas LIFU to AI affected EEG amplitudes around 500 milliseconds. In addition, only LIFU to the AI affected HRV as indexed by an increase in standard deviation of N-N intervals (SDNN) and mean HRV low frequency power. There was no effect of LIFU to either AI or PI on EDR or blood pressure. Taken together, LIFU looks to be an effective method to individually target sub-regions of the insula in humans for site-specific effects on brain biomarkers of pain processing and autonomic reactivity that translates to reduced perceived pain to a transient heat stimulus. These data have implications for the treatment of chronic pain and several neuropsychological diseases like anxiety, depression and addiction that all demonstrate abnormal activity in the insula concomitant with dysregulated autonomic function.

Experimental induction of peritraumatic dissociation: The role of negative affect and pain and their psychophysiological and neural correlates

While research has elucidated processes underlying dissociative symptoms in patients with posttraumatic stress disorder, little is known about the circumstances under which trauma-related dissociation initially arises. To experimentally investigate causes and concomitants of peritraumatic dissociation, we subjected sixty-nine healthy women to aversive-audiovisual and painful-electrical stimulation in a 2(aversive/neutral film) x 2(pain/no pain) within-subject design while recording psychophysiological and fMRI-BOLD responses. Afterwards, participants rated negative-affect, pain, and dissociation for each condition. Using Bayesian multilevel regression models, we examined (1) whether aversive-audiovisual and painful-electrical stimulation elicit higher dissociation-levels than control conditions and (2) whether stronger negative-affect and pain responses (operationalized via self-report, psychophysiological, and neural markers) correlate with higher dissociation-levels. Several key findings emerged: Both aversive-audiovisual and painful-electrical stimulation elicited dissociation. Dissociation was linked to higher self-reported negative-affect, but we did not find enough evidence linking it to psychophysiological and neural negative-affect markers. However, dissociation was associated with higher levels of self-reported pain, a skin-conductance-response-based pain marker, and the fMRI-BOLD-based Neurologic-Pain-Signature. Results indicate that both aversive-audiovisual and painful stimuli can independently cause dissociation. Critically, pain responses captured via self-report, psychophysiological, and neural markers were consistently linked to higher dissociation-levels suggesting a specific, evolutionary meaningful, contribution of pain to the rise of dissociation.

A somato-cognitive action network alternates with effector regions in motor cortex

Motor cortex (M1) has been thought to form a continuous somatotopic homunculus extending down the precentral gyrus from foot to face representations1,2, despite evidence for concentric functional zones3 and maps of complex actions4. Here, using precision functional magnetic resonance imaging (fMRI) methods, we find that the classic homunculus is interrupted by regions with distinct connectivity, structure and function, alternating with effector-specific (foot, hand and mouth) areas. These inter-effector regions exhibit decreased cortical thickness and strong functional connectivity to each other, as well as to the cingulo-opercular network (CON), critical for action5 and physiological control6, arousal7, errors8 and pain9. This interdigitation of action control-linked and motor effector regions was verified in the three largest fMRI datasets. Macaque and pediatric (newborn, infant and child) precision fMRI suggested cross-species homologues and developmental precursors of the inter-effector system. A battery of motor and action fMRI tasks documented concentric effector somatotopies, separated by the CON-linked inter-effector regions. The inter-effectors lacked movement specificity and co-activated during action planning (coordination of hands and feet) and axial body movement (such as of the abdomen or eyebrows). These results, together with previous studies demonstrating stimulation-evoked complex actions4 and connectivity to internal organs10 such as the adrenal medulla, suggest that M1 is punctuated by a system for whole-body action planning, the somato-cognitive action network (SCAN). In M1, two parallel systems intertwine, forming an integrate-isolate pattern: effector-specific regions (foot, hand and mouth) for isolating fine motor control and the SCAN for integrating goals, physiology and body movement.

The Biology of Pain: Through the Rheumatology Lens

Chronic pain is a major socioeconomic burden globally. The most frequent origin of chronic pain is musculoskeletal. In inflammatory musculoskeletal diseases such as rheumatoid arthritis (RA), chronic pain is a primary determinant of deleterious quality of life. The pivotal role of peripheral inflammation in the initiation and perpetuation of nociceptive pain is well-established among patients with musculoskeletal diseases. However, the persistence of pain, even after the apparent resolution of peripheral inflammation, alludes to the coexistence of different pain states. Recent advances in neurobiology have highlighted the importance of nociplastic pain mechanisms. In this review we aimed to explore the biology of pain with a particular focus on nociplastic pain in RA.

DeepComBat: A Statistically Motivated, Hyperparameter-Robust, Deep Learning Approach to Harmonization of Neuroimaging Data

Neuroimaging data from multiple batches (i.e. acquisition sites, scanner manufacturer, datasets, etc.) are increasingly necessary to gain new insights into the human brain. However, multi-batch data, as well as extracted radiomic features, exhibit pronounced technical artifacts across batches. These batch effects introduce confounding into the data and can obscure biological effects of interest, decreasing the generalizability and reproducibility of findings. This is especially true when multi-batch data is used alongside complex downstream analysis models, such as machine learning methods. Image harmonization methods seeking to remove these batch effects are important for mitigating these issues; however, significant multivariate batch effects remain in the data following harmonization by current state-of-the-art statistical and deep learning methods. We present DeepCombat, a deep learning harmonization method based on a conditional variational autoencoder architecture and the ComBat harmonization model. DeepCombat learns and removes subject-level batch effects by accounting for the multivariate relationships between features. Additionally, DeepComBat relaxes a number of strong assumptions commonly made by previous deep learning harmonization methods and is empirically robust across a wide range of hyperparameter choices. We apply this method to neuroimaging data from a large cognitive-aging cohort and find that DeepCombat outperforms existing methods, as assessed by a battery of machine learning methods, in removing scanner effects from cortical thickness measurements while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically-motivated deep learning harmonization methods.

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.

Distraction and cognitive control independently impact parietal and prefrontal response to pain

Previous studies have found that distracting someone through a challenging activity leads to hypoalgesia, an effect mediated by parietal and prefrontal processes. Other studies suggest that challenging activities affect the ability to regulate one's aching experiences, due to the partially common neural substrate between cognitive control and pain at the level of the medial prefrontal cortex. We investigated the effects of distraction and cognitive control on pain by delivering noxious stimulations during or after a Stroop paradigm (requiring high cognitive load) or a neutral condition. We found less-intense and unpleasant subjective pain ratings during (compared to after) task execution. This hypoalgesia was associated with enhanced activity at the level of the dorsolateral prefrontal cortex and the posterior parietal cortex, which also showed negative connectivity with the insula. Furthermore, multivariate pattern analysis revealed that distraction altered the neural response to pain, by making it more similar to that associated with previous Stroop tasks. All these effects were independent of the nature of the task, which, instead, led to a localized neural modulation around the anterior cingulate cortex. Overall, our study underscores the role played by two facets of human executive functions, which exert an independent influence on the neural response to pain.

Prefrontal engrams of long-term fear memory perpetuate pain perception

A painful episode can lead to a life-long increase in an individual's experience of pain. Fearful anticipation of imminent pain could play a role in this phenomenon, but the neurobiological underpinnings are unclear because fear can both suppress and enhance pain. Here, we show in mice that long-term associative fear memory stored in neuronal engrams in the prefrontal cortex determines whether a painful episode shapes pain experience later in life. Furthermore, under conditions of inflammatory and neuropathic pain, prefrontal fear engrams expand to encompass neurons representing nociception and tactile sensation, leading to pronounced changes in prefrontal connectivity to fear-relevant brain areas. Conversely, silencing prefrontal fear engrams reverses chronically established hyperalgesia and allodynia. These results reveal that a discrete subset of prefrontal cortex neurons can account for the debilitating comorbidity of fear and chronic pain and show that attenuating the fear memory of pain can alleviate chronic pain itself.

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size.

Both fine-grained and coarse-grained spatial patterns of neural activity measured by functional MRI show preferential encoding of pain in the human brain

How pain emerges from human brain remains an unresolved question in pain neuroscience. Neuroimaging studies have suggested that all brain areas activated by painful stimuli were also activated by tactile stimuli, and vice versa. Nonetheless, pain-preferential spatial patterns of voxel-level activation in the brain have been observed when distinguishing painful and tactile brain activations using multivariate pattern analysis (MVPA). According to two hypotheses, the neural activity pattern preferentially encoding pain could exist at a global, coarse-grained, regional level, corresponding to the "pain connectome" hypothesis proposing that pain-preferential information may be encoded by the synchronized activity across multiple distant brain regions, and/or exist at a local, fine-grained, voxel level, corresponding to the "intermingled specialized/preferential neurons" hypothesis proposing that neurons responding specially or preferentially to pain could be present and intermingled with non-pain neurons within a voxel. Here, we systematically investigated the spatial scales of pain-distinguishing information in the human brain measured by fMRI using machine learning techniques, and found that pain-distinguishing information could be detected at both coarse-grained spatial scales across widely distributed brain regions and fine-grained spatial scales within many local areas. Importantly, the spatial distribution of pain-distinguishing information in the brain varies across individuals and such inter-individual variations may be related to a person's trait about pain perception, particularly the pain vigilance and awareness. These results provide new insights into the long-standing question of how pain is represented in the human brain and help the identification of characteristic neuroimaging measurements of pain.

Doctor trustworthiness influences pain and its neural correlates in virtual medical interactions

Trust is an important component of the doctor-patient relationship and is associated with improved patient satisfaction and health outcomes. Previously, we reported that patient feelings of trust and similarity toward their clinician predicted reductions in evoked pain in response to painful heat stimulations. In the present study, we investigated the brain mechanisms underlying this effect. We used face stimuli previously developed using a data-driven computational modeling approach that differ in perceived trustworthiness and superimposed them on bodies dressed in doctors' attire. During functional magnetic resonance imaging, participants (n = 42) underwent a series of virtual medical interactions with these doctors during which they received painful heat stimulation as an analogue of a painful diagnostic procedure. Participants reported increased pain when receiving painful heat stimulations from low-trust doctors, which was accompanied by increased activity in pain-related brain regions and a multivariate pain-predictive neuromarker. Findings suggest that patient trust in their doctor may have tangible impacts on pain and point to a potential brain basis for trust-related reductions in pain through the modulation of brain circuitry associated with the sensory-discriminative and affective-motivational dimensions of pain.

Graph theory analysis reveals an assortative pain network vulnerable to attacks

The neural substrate of pain experience has been described as a dense network of connected brain regions. However, the connectivity pattern of these brain regions remains elusive, precluding a deeper understanding of how pain emerges from the structural connectivity. Here, we use graph theory to systematically characterize the architecture of a comprehensive pain network, including both cortical and subcortical brain areas. This structural brain network consists of 49 nodes denoting pain-related brain areas, linked by edges representing their relative incoming and outgoing axonal projection strengths. Sixty-three percent of brain areas in this structural pain network share reciprocal connections, reflecting a dense network. The clustering coefficient, a measurement of the probability that adjacent nodes are connected, indicates that brain areas in the pain network tend to cluster together. Community detection, the process of discovering cohesive groups in complex networks, successfully reveals two known subnetworks that specifically mediate the sensory and affective components of pain, respectively. Assortativity analysis, which evaluates the tendency of nodes to connect with other nodes with similar features, indicates that the pain network is assortative. Finally, robustness, the resistance of a complex network to failures and perturbations, indicates that the pain network displays a high degree of error tolerance (local failure rarely affects the global information carried by the network) but is vulnerable to attacks (selective removal of hub nodes critically changes network connectivity). Taken together, graph theory analysis unveils an assortative structural pain network in the brain processing nociceptive information, and the vulnerability of this network to attack opens up the possibility of alleviating pain by targeting the most connected brain areas in the network.

Cerebral cortical hemodynamic metrics to aid in assessing pain levels? A pilot study of functional near-infrared spectroscopy

Introduction

Establishing an accurate way to quantify pain is one of the most formidable tasks in neuroscience and medical practice. Functional near-infrared spectroscopy (fNIRS) can be utilized to detect the brain’s reaction to pain. The study sought to assess the neural mechanisms of the wrist-ankle acupuncture transcutaneous electrical nerve stimulation analgesic bracelet (E-WAA) in providing pain relief and altering cerebral blood volume dynamics, and to ascertain the reliability of cortical activation patterns as a means of objectively measuring pain.

Methods

The participants (mean age 36.6 ± 7.2 years) with the cervical-shoulder syndrome (CSS) underwent pain testing prior to, 1 min following, and 30 min after the left point Jianyu treatment. The E-WAA was used to administer an electrical stimulation therapy that lasted for 5 min. A 24-channel fNIRS system was utilized to monitor brain oxyhemoglobin (HbO) levels, and changes in HbO concentrations, cortical activation areas, and subjective pain assessment scales were documented.

Results

We discovered that HbO concentrations in the prefrontal cortex significantly increased when CSS patients were exposed to painful stimuli at the cerebral cortex level. The second pain test saw a considerable decrease in the average HbO change amount in the prefrontal cortex when E-WAA was applied, which in turn led to a reduction in the amount of activation and the size of the activated area in the cortex.

Discussion

This study revealed that the frontal polar (FP) and dorsolateral prefrontal cortex (DLPFC) were linked to the analgesic modulation activated by the E-WAA.

Evidence for embracing normative modeling

In this work, we expand the normative model repository introduced in Rutherford et al., 2022a to include normative models charting lifespan trajectories of structural surface area and brain functional connectivity, measured using two unique resting-state network atlases (Yeo-17 and Smith-10), and an updated online platform for transferring these models to new data sources. We showcase the value of these models with a head-to-head comparison between the features output by normative modeling and raw data features in several benchmarking tasks: mass univariate group difference testing (schizophrenia versus control), classification (schizophrenia versus control), and regression (predicting general cognitive ability). Across all benchmarks, we show the advantage of using normative modeling features, with the strongest statistically significant results demonstrated in the group difference testing and classification tasks. We intend for these accessible resources to facilitate the wider adoption of normative modeling across the neuroimaging community.

Characterization of spatiotemporal dynamics of binary and graded tonic pain in humans using intracranial recordings

Pain is a complex experience involving sensory, emotional, and cognitive aspects, and multiple networks manage its processing in the brain. Examining how pain transforms into a behavioral response can shed light on the networks' relationships and facilitate interventions to treat chronic pain. However, studies using high spatial and temporal resolution methods to investigate the neural encoding of pain and its psychophysical correlates have been limited. We recorded from intracranial stereo-EEG (sEEG) electrodes implanted in sixteen different brain regions of twenty patients who underwent psychophysical pain testing consisting of a tonic thermal stimulus to the hand. Broadband high-frequency local field potential amplitude (HFA; 70-150 Hz) was isolated to investigate the relationship between the ongoing neural activity and the resulting psychophysical pain evaluations. Two different generalized linear mixed-effects models (GLME) were employed to assess the neural representations underlying binary and graded pain psychophysics. The first model examined the relationship between HFA and whether the patient responded "yes" or "no" to whether the trial was painful. The second model investigated the relationship between HFA and how painful the stimulus was rated on a visual analog scale. GLMEs revealed that HFA in the inferior temporal gyrus (ITG), superior frontal gyrus (SFG), and superior temporal gyrus (STG) predicted painful responses at stimulus onset. An increase in HFA in the orbitofrontal cortex (OFC), SFG, and striatum predicted pain responses at stimulus offset. Numerous regions including the anterior cingulate cortex, hippocampus, IFG, MTG, OFC, and striatum, predicted the pain rating at stimulus onset. However, only the amygdala and fusiform gyrus predicted increased pain ratings at stimulus offset. We characterized the spatiotemporal representations of binary and graded painful responses during tonic pain stimuli. Our study provides evidence from intracranial recordings that the neural encoding of psychophysical pain changes over time during a tonic thermal stimulus, with different brain regions being predictive of pain at the beginning and end of the stimulus.

Case-by-case: neural markers of emotion and task stimulus significance

The present study assessed the hypothesis that electrophysiological markers of emotional and task stimulus significance can be demonstrated in concert at the level of the individual case. Participants (n = 18, 9 females) viewed low and high-arousing pictures selected from behavior systems of sexual reproduction, disease avoidance, and predator fear. Furthermore, to concurrently manipulate task relevance, participants performed an explicit emotion categorization task with either low or high-arousing pictures alternating as target stimuli in separate experimental blocks. Pooled across behavior systems, event-related components sensitive to emotional significance reached statistical significance in 100% of the tests for the early posterior negativity and in 96% of the tests for the late positive potential. Regarding explicit task relevance, the target P3 effect was significant in 96% of the tests. These findings demonstrate that neural markers of stimulus significance driven by emotional picture content and explicit task demands can be assessed at the individual level. Replicating an effect case-after-case provides strong support for an effect common-to-all and may support individual inferences. Contributions of the case-by-case approach to reveal reproducible effects and implications for the development of neural biomarkers for specific affective and cognitive component processes are discussed.

Neuro-computational mechanisms and individual biases in action-outcome learning under moral conflict

Learning to predict action outcomes in morally conflicting situations is essential for social decision-making but poorly understood. Here we tested which forms of Reinforcement Learning Theory capture how participants learn to choose between self-money and other-shocks, and how they adapt to changes in contingencies. We find choices were better described by a reinforcement learning model based on the current value of separately expected outcomes than by one based on the combined historical values of past outcomes. Participants track expected values of self-money and other-shocks separately, with the substantial individual difference in preference reflected in a valuation parameter balancing their relative weight. This valuation parameter also predicted choices in an independent costly helping task. The expectations of self-money and other-shocks were biased toward the favored outcome but fMRI revealed this bias to be reflected in the ventromedial prefrontal cortex while the pain-observation network represented pain prediction errors independently of individual preferences.

Resting-state neural mechanisms of capability for suicide and their interaction with pain - A CAN-BIND-05 Study

BACKGROUND

Suicidal ideation is highly prevalent in Major Depressive Disorder (MDD). However, the factors determining who will transition from ideation to attempt are not established. Emerging research points to suicide capability (SC), which reflects fearlessness of death and increased pain tolerance, as a construct mediating this transition. This Canadian Biomarker Integration Network in Depression study (CANBIND-5) aimed to identify the neural basis of SC and its interaction with pain as a marker of suicide attempt.

METHODS

MDD patients (n = 20) with suicide risk and healthy controls (n = 21) completed a self-report SC scale and a cold pressor task measuring pain threshold, tolerance, endurance, and intensity at threshold and tolerance. All participants underwent a resting-state brain scan and functional connectivity was examined for 4 regions: anterior insula (aIC), posterior insula (pIC), anterior mid-cingulate cortex (aMCC) and subgenual anterior cingulate cortex (sgACC).

RESULTS

In MDD, SC correlated positively with pain endurance and negatively with threshold intensity. Furthermore, SC correlated with the connectivity of aIC to the supramarginal gyrus, pIC to the paracingulate gyrus, aMCC to the paracingulate gyrus, and sgACC to the dorsolateral prefrontal cortex. These correlations were stronger in MDD compared to controls. Only threshold intensity mediated the correlation between SC and connectivity strength.

LIMITATIONS

Resting-state scans provided an indirect assessment of SC and the pain network.

CONCLUSIONS

These findings highlight point to a neural network underlying SC that is associated with pain processing. This supports the potential clinical utility of pain response measurement as a method to investigate markers of suicide risk.

A multivariate brain signature for reward

The processing of reinforcers and punishers is crucial to adapt to an ever changing environment and its dysregulation is prevalent in mental health and substance use disorders. While many human brain measures related to reward have been based on activity in individual brain regions, recent studies indicate that many affective and motivational processes are encoded in distributed systems that span multiple regions. Consequently, decoding these processes using individual regions yields small effect sizes and limited reliability, whereas predictive models based on distributed patterns yield larger effect sizes and excellent reliability. To create such a predictive model for the processes of rewards and losses, termed the Brain Reward Signature (BRS), we trained a model to predict the signed magnitude of monetary rewards on the Monetary Incentive Delay task (MID; N = 39) and achieved a highly significant decoding performance (92% for decoding rewards versus losses). We subsequently demonstrate the generalizability of our signature on another version of the MID in a different sample (92% decoding accuracy; N = 12) and on a gambling task from a large sample (73% decoding accuracy, N = 1084). We further provided preliminary data to characterize the specificity of the signature by illustrating that the signature map generates estimates that significantly differ between rewarding and negative feedback (92% decoding accuracy) but do not differ for conditions that differ in disgust rather than reward in a novel Disgust-Delay Task (N = 39). Finally, we show that passively viewing positive and negatively valenced facial expressions loads positively on our signature, in line with previous studies on morbid curiosity. We thus created a BRS that can accurately predict brain responses to rewards and losses in active decision making tasks, and that possibly relates to information seeking in passive observational tasks.

Analgesic Electrical Stimulation Combined with Wrist-Ankle Acupuncture Reduces the Cortical Response to Pain in Patients with Myofasciitis: A Randomized Clinical Trial

OBJECTIVE

Transcutaneous electrical nerve stimulation (TENS) based on wrist-ankle acupuncture has been shown to relieve pain levels in patients with myofascial pain syndrome (MPS). However, its efficacy is highly subjective. The purpose of this study was to evaluate the feasibility and effectiveness of TENS based on wrist-ankle acupuncture for pain management in patients with MPS from the perspective of cerebral cortex hemodynamics.

DESIGN, SETTING, PARTICIPANTS AND INTERVENTIONS

We designed a double-blind, randomized, controlled clinical trial. Thirty-one male patients with MPS were randomly assigned to two parallel groups. The experimental group (n = 16) received TENS based on wrist-ankle acupuncture for analgesic treatment, while the control group (n = 15) did not. The pain was induced by mechanically pressurized at acupoint Jianjing. The multichannel functional near-infrared spectroscopy (fNIRS) equipment was utilized for measuring oxyhemoglobin (HbO) levels in the cerebral cortex during the tasks.

RESULTS

After the intervention, visual analog scale (VAS), the activation degree and activation area of pain perception cortices were significantly reduced in the experimental group compared to the baseline values (P < .05). Particularly, Frontopolar Area (FPA), and Dorsolateral Prefrontal Cortex (DLPFC) are highly involved in the pain process and pain modulation.

CONCLUSION

Compared to no intervention, TENS based on wrist-ankle acupuncture can be effective in relieving pain in patients with MPS in terms of cerebral cortical hemodynamics. However, further studies are necessary to quantify the analgesic effect in terms of cerebral hemodynamics and brain activation.

Functional magnetic resonance imaging of headache: Issues, best-practices, and new directions, a narrative review

OBJECTIVE

To ensure readers are informed consumers of functional magnetic resonance imaging (fMRI) research in headache, to outline ongoing challenges in this area of research, and to describe potential considerations when asked to collaborate on fMRI research in headache, as well as to suggest future directions for improvement in the field.

BACKGROUND

Functional MRI has played a key role in understanding headache pathophysiology, and mapping networks involved with headache-related brain activity have the potential to identify intervention targets. Some investigators have also begun to explore its use for diagnosis.

METHODS/RESULTS

The manuscript is a narrative review of the current best practices in fMRI in headache research, including guidelines on transparency and reproducibility. It also contains an outline of the fundamentals of MRI theory, task-related study design, resting-state functional connectivity, relevant statistics and power analysis, image preprocessing, and other considerations essential to the field.

CONCLUSION

Best practices to increase reproducibility include methods transparency, eliminating error, using a priori hypotheses and power calculations, using standardized instruments and diagnostic criteria, and developing large-scale, publicly available datasets.

A machine learning based approach towards high-dimensional mediation analysis

Mediation analysis is used to investigate the role of intermediate variables (mediators) that lie in the path between an exposure and an outcome variable. While significant research has focused on developing methods for assessing the influence of mediators on the exposure-outcome relationship, current approaches do not easily extend to settings where the mediator is high-dimensional. These situations are becoming increasingly common with the rapid increase of new applications measuring massive numbers of variables, including brain imaging, genomics, and metabolomics. In this work, we introduce a novel machine learning based method for identifying high dimensional mediators. The proposed algorithm iterates between using a machine learning model to map the high-dimensional mediators onto a lower-dimensional space, and using the predicted values as input in a standard three-variable mediation model. Hence, the machine learning model is trained to maximize the likelihood of the mediation model. Importantly, the proposed algorithm is agnostic to the machine learning model that is used, providing significant flexibility in the types of situations where it can be used. We illustrate the proposed methodology using data from two functional Magnetic Resonance Imaging (fMRI) studies. First, using data from a task-based fMRI study of thermal pain, we combine the proposed algorithm with a deep learning model to detect distributed, network-level brain patterns mediating the relationship between stimulus intensity (temperature) and reported pain at the single trial level. Second, using resting-state fMRI data from the Human Connectome Project, we combine the proposed algorithm with a connectome-based predictive modeling approach to determine brain functional connectivity measures that mediate the relationship between fluid intelligence and working memory accuracy. In both cases, our multivariate mediation model links exposure variables (thermal pain or fluid intelligence), high dimensional brain measures (single-trial brain activation maps or resting-state brain connectivity) and behavioral outcomes (pain report or working memory accuracy) into a single unified model. Using the proposed approach, we are able to identify brain-based measures that simultaneously encode the exposure variable and correlate with the behavioral outcome.

An Interpretable and Predictive Connectivity-Based Neural Signature for Chronic Cannabis Use

BACKGROUND

Cannabis is one of the most widely used substances in the world, with usage trending upward in recent years. However, although the psychiatric burden associated with maladaptive cannabis use has been well established, reliable and interpretable biomarkers associated with chronic use remain elusive. In this study, we combine large-scale functional magnetic resonance imaging with machine learning and network analysis and develop an interpretable decoding model that offers both accurate prediction and novel insights into chronic cannabis use.

METHODS

Chronic cannabis users (n = 166) and nonusing healthy control subjects (n = 124) completed a cue-elicited craving task during functional magnetic resonance imaging. Linear machine learning methods were used to classify individuals into chronic users and nonusers based on whole-brain functional connectivity. Network analysis was used to identify the most predictive regions and communities.

RESULTS

We obtained high (∼80% out-of-sample) accuracy across 4 different classification models, demonstrating that task-evoked connectivity can successfully differentiate chronic cannabis users from nonusers. We also identified key predictive regions implicating motor, sensory, attention, and craving-related areas, as well as a core set of brain networks that contributed to successful classification. The most predictive networks also strongly correlated with cannabis craving within the chronic user group.

CONCLUSIONS

This novel approach produced a neural signature of chronic cannabis use that is both accurate in terms of out-of-sample prediction and interpretable in terms of predictive networks and their relation to cannabis craving.

Angiotensin II Regulates the Neural Expression of Subjective Fear in Humans: A Precision Pharmaco-Neuroimaging Approach

BACKGROUND

Rodent models and pharmacological neuroimaging studies in humans have been used to test novel pharmacological agents to reduce fear. However, these strategies are limited with respect to determining process-specific effects on the actual subjective experience of fear, which represents the key symptom that motivates patients to seek treatment. In this study, we used a novel precision pharmacological functional magnetic resonance imaging approach based on process-specific neuroaffective signatures to determine effects of the selective angiotensin II type 1 receptor (AT1R) antagonist losartan on the subjective experience of fear.

METHODS

In a double-blind, placebo-controlled, randomized pharmacological functional magnetic resonance imaging design, healthy participants (N = 87) were administered 50 mg losartan or placebo before they underwent an oddball paradigm that included neutral, novel, and fear oddballs. Effects of losartan on brain activity and connectivity as well as on process-specific multivariate neural signatures were examined.

RESULTS

AT1R blockade selectively reduced neurofunctional reactivity to fear-inducing visual oddballs in terms of attenuating dorsolateral prefrontal activity and amygdala-ventral anterior cingulate communication. Neurofunctional decoding further demonstrated fear-specific effects in that AT1R blockade reduced the neural expression of subjective fear but not of threat or nonspecific negative affect and did not influence reactivity to novel oddballs.

CONCLUSIONS

These results show a specific role of the AT1R in regulating the subjective fear experience and demonstrate the feasibility of a precision pharmacological functional magnetic resonance imaging approach to the affective characterization of novel receptor targets for fear in humans.

An fMRI-Based Brain Marker of Individual Differences in Delay Discounting

Individual differences in delay discounting-how much we discount future compared to immediate rewards-are associated with general life outcomes, psychopathology, and obesity. Here, we use machine learning on fMRI activity during an intertemporal choice task to develop a functional brain marker of these individual differences in human adults. Training and cross-validating the marker in one dataset (Study 1, N = 110 male adults) resulted in a significant prediction-outcome correlation (r = 0.49), generalized to predict individual differences in a completely independent dataset (Study 2: N = 145 male and female adults, r = 0.45), and predicted discounting several weeks later. Out-of-sample responses of the functional brain marker, but not discounting behavior itself, differed significantly between overweight and lean individuals in both studies, and predicted fasting-state blood levels of insulin, c-peptide, and leptin in Study 1. Significant predictive weights of the marker were found in cingulate, insula, and frontoparietal areas, among others, suggesting an interplay among regions associated with valuation, conflict processing, and cognitive control. This new functional brain marker is a step toward a generalizable brain model of individual differences in delay discounting. Future studies can evaluate it as a potential transdiagnostic marker of altered decision-making in different clinical and developmental populations.SIGNIFICANCE STATEMENT People differ substantially in how much they prefer smaller sooner rewards or larger later rewards such as spending money now versus saving it for retirement. These individual differences are generally stable over time and have been related to differences in mental and bodily health. What is their neurobiological basis? We applied machine learning to brain-imaging data to identify a novel brain activity pattern that accurately predicts how much people prefer sooner versus later rewards, and which can be used as a new brain-based measure of intertemporal decision-making in future studies. The resulting functional brain marker also predicts overweight and metabolism-related blood markers, providing new insight into the possible links between metabolism and the cognitive and brain processes involved in intertemporal decision-making.

Social Touch Reduces Pain Perception—An fMRI Study of Cortical Mechanisms

Unmyelinated low-threshold mechanoreceptors (C-tactile, CT) in the human skin are important for signaling information about hedonic aspects of touch. We have previously reported that CT-targeted brush stroking by means of a robot reduces experimental mechanical pain. To improve the ecological validity of the stimulation, we developed standardized human–human touch gestures for signaling attention and calming. The attention gesture is characterized by tapping of the skin and is perceived as neither pleasant nor unpleasant, i.e., neutral. The calming gesture is characterized by slow stroking of the skin and is perceived as moderately to very pleasant. Furthermore, the attention (tapping) gesture is ineffective, whereas the calming (stroking) gesture is effective in activating CT-afferents. We conducted an fMRI study (n = 32) and capitalized on the previous development of touch gestures. We also developed an MR compatible stimulator for high-precision mechanical pain stimulation of the thenar region of the hand. Skin-to-skin touching (stroking or tapping) was applied and was followed by low and high pain. When the stroking gesture preceded pain, the pain was rated as less intense. When the tapping gesture preceded the pain, the pain was rated as more intense. Individual pain perception related to insula activation, but the activation was not higher for stroking than for tapping in any brain area during the stimulation period. However, during the evaluation period, stronger activation in the periaqueductal gray matter was observed after calming touch compared to after tapping touch. This finding invites speculation that human–human gentle skin stroking, effective in activating CT-afferents, reduced pain through neural processes involving CT-afferents and the descending pain pathway.

Toward the unknown: consciousness and pain

Studies of consciousness are hindered by the complexity of the brain, but it is possible to study the consciousness of a sensation, namely pain. Three systems are necessary to experience pain: the somatosensory system conveys information about an injury to the thalamus where an awareness of the injury but not the painfulness emerges. The thalamus distributes the information to the affective system, which modulates the intensity of the pain, and to the cognitive system that imparts attention to the pain. Imaging of patients in pain and those experiencing placebo and hypnosis-induced analgesia shows that two essential cortical circuits for pain and attention are located within the anterior cingulate cortex. The circuits are activated when a high-frequency input results in the development of a long-term potentiation (LTP) at synapses on the apical dendrites of pyramidal neurons. The LTP acts via α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and N-methyl-D-aspartate (NMDA) receptors, and an anterior cingulate cortex-specific type-1 adenylate cyclase is necessary for both the LTP and the pain. The apical dendrites form an extensive network such that the input from serious injuries results in the emergence of a local field potential. Using mouse models, I propose experiments designed to test the hypothesis that the local field potential is necessary and sufficient for the consciousness of pain.

A novel thermoelectric device integrated with a psychophysical paradigm to study pain processing in human subjects

INTRODUCTION

Cerebral projections of nociceptive stimuli are of great interest as targets for neuromodulation in chronic pain. To study cerebral networks involved in processing noxious stimuli, researchers often rely on thermo-nociception to induce pain. However, various limitations exist in many pain-inducing techniques, such as not accounting for individual variations in pain and trial structure predictability.

METHODS

We propose an improved and reliable psychometric experimental method to evaluate human nociceptive processing to overcome some of these limitations. The developed testing paradigm leverages a custom-built, open-source, thermoelectric device (TED). The device construction and hardware are described. A maximum-likelihood adaptive algorithm is integrated into the TED software, facilitating individual psychometric functions representative of both hot and cold pain perception. In addition to testing only hot or cold thresholds, the TED may also be used to induce the thermal grill illusion (TGI), where the bars are set to alternating warm and cool temperatures.

RESULTS

Here, we validated the TED's capability to adjust between different temperatures and showed that the device quickly and automatically changes temperature without any experimenter input. We also validated the device and integrated psychometric pain task in 21 healthy human subjects. Hot and cold pain thresholds (HPT, CPT) were determined in human subjects with <1 °C of variation. Thresholds were anticorrelated, meaning a volunteer with a low CPT likely had a high HPT. We also showed how the TED can be used to induce the TGI.

CONCLUSION

The TED can induce thermo-nociception and provide probabilistic measures of hot and cold pain thresholds. Based on the findings presented, we discuss how the TED could be used to study thermo-nociceptive cerebral projections if paired with intracranial electrode monitoring.

Measuring pain and nociception: Through the glasses of a computational scientist. Transdisciplinary overview of methods

In a healthy state, pain plays an important role in natural biofeedback loops and helps to detect and prevent potentially harmful stimuli and situations. However, pain can become chronic and as such a pathological condition, losing its informative and adaptive function. Efficient pain treatment remains a largely unmet clinical need. One promising route to improve the characterization of pain, and with that the potential for more effective pain therapies, is the integration of different data modalities through cutting edge computational methods. Using these methods, multiscale, complex, and network models of pain signaling can be created and utilized for the benefit of patients. Such models require collaborative work of experts from different research domains such as medicine, biology, physiology, psychology as well as mathematics and data science. Efficient work of collaborative teams requires developing of a common language and common level of understanding as a prerequisite. One of ways to meet this need is to provide easy to comprehend overviews of certain topics within the pain research domain. Here, we propose such an overview on the topic of pain assessment in humans for computational researchers. Quantifications related to pain are necessary for building computational models. However, as defined by the International Association of the Study of Pain (IASP), pain is a sensory and emotional experience and thus, it cannot be measured and quantified objectively. This results in a need for clear distinctions between nociception, pain and correlates of pain. Therefore, here we review methods to assess pain as a percept and nociception as a biological basis for this percept in humans, with the goal of creating a roadmap of modelling options.

Effects of remifentanil on brain responses to noxious stimuli during deep propofol sedation

BACKGROUND

The safety of anaesthesia has improved as a result of better control of anaesthetic depth. However, conventional monitoring does not inform on the nature of nociceptive processes during unconsciousness. A means of inferring the quality of potentially painful experiences could derive from analysis of brain activity using neuroimaging. We have evaluated the dose effects of remifentanil on brain response to noxious stimuli during deep sedation and spontaneous breathing.

METHODS

Optimal data were obtained in 26 healthy subjects. Pressure stimulation that proved to be moderately painful before the experiment was applied to the thumbnail. Functional MRI was acquired in 4-min periods at low (0.5 ng ml^-1), medium (1 ng ml^-1), and high (1.5 ng ml^-1) target plasma concentrations of remifentanil at a stable background infusion of propofol adjusted to induce a state of light unconsciousness.

RESULTS

At low remifentanil doses, we observed partial activation in brain areas processing sensory-discriminative and emotional-affective aspects of pain. At medium doses, relevant changes were identified in structures highly sensitive to general brain arousal, including the brainstem, cerebellum, thalamus, auditory and visual cortices, and the frontal lobe. At high doses, no significant activation was observed.

CONCLUSIONS

The response to moderately intense focal pressure in pain-related brain networks is effectively eliminated with safe remifentanil doses. However, the safety margin in deep sedation-analgesia would be narrowed in minimising not only nociceptive responses, but also arousal-related biological stress.

Optimizing and Accelerating the Development of Precision Pain Treatments for Chronic Pain: IMMPACT Review and Recommendations

Large variability in the individual response to even the most-efficacious pain treatments is observed clinically, which has led to calls for a more personalized, tailored approach to treating patients with pain (ie, "precision pain medicine"). Precision pain medicine, currently an aspirational goal, would consist of empirically based algorithms that determine the optimal treatments, or treatment combinations, for specific patients (ie, targeting the right treatment, in the right dose, to the right patient, at the right time). Answering this question of "what works for whom" will certainly improve the clinical care of patients with pain. It may also support the success of novel drug development in pain, making it easier to identify novel treatments that work for certain patients and more accurately identify the magnitude of the treatment effect for those subgroups. Significant preliminary work has been done in this area, and analgesic trials are beginning to utilize precision pain medicine approaches such as stratified allocation on the basis of prespecified patient phenotypes using assessment methodologies such as quantitative sensory testing. Current major challenges within the field include: 1) identifying optimal measurement approaches to assessing patient characteristics that are most robustly and consistently predictive of inter-patient variation in specific analgesic treatment outcomes, 2) designing clinical trials that can identify treatment-by-phenotype interactions, and 3) selecting the most promising therapeutics to be tested in this way. This review surveys the current state of precision pain medicine, with a focus on drug treatments (which have been most-studied in a precision pain medicine context). It further presents a set of evidence-based recommendations for accelerating the application of precision pain methods in chronic pain research. PERSPECTIVE: Given the considerable variability in treatment outcomes for chronic pain, progress in precision pain treatment is critical for the field. An array of phenotypes and mechanisms contribute to chronic pain; this review summarizes current knowledge regarding which treatments are most effective for patients with specific biopsychosocial characteristics.

A neuromarker for drug and food craving distinguishes drug users from non-users

Craving is a core feature of substance use disorders. It is a strong predictor of substance use and relapse and is linked to overeating, gambling, and other maladaptive behaviors. Craving is measured via self-report, which is limited by introspective access and sociocultural contexts. Neurobiological markers of craving are both needed and lacking, and it remains unclear whether craving for drugs and food involve similar mechanisms. Across three functional magnetic resonance imaging studies (n = 99), we used machine learning to identify a cross-validated neuromarker that predicts self-reported intensity of cue-induced drug and food craving (P < 0.0002). This pattern, which we term the Neurobiological Craving Signature (NCS), includes ventromedial prefrontal and cingulate cortices, ventral striatum, temporal/parietal association areas, mediodorsal thalamus and cerebellum. Importantly, NCS responses to drug versus food cues discriminate drug users versus non-users with 82% accuracy. The NCS is also modulated by a self-regulation strategy. Transfer between separate neuromarkers for drug and food craving suggests shared neurobiological mechanisms. Future studies can assess the discriminant and convergent validity of the NCS and test whether it responds to clinical interventions and predicts long-term clinical outcomes.

The value of brain MRI functional connectivity data in a machine learning classifier for distinguishing migraine from persistent post-traumatic headache

Background

Post-traumatic headache (PTH) and migraine often have similar phenotypes. The objective of this exploratory study was to develop classification models to differentiate persistent PTH (PPTH) from migraine using clinical data and magnetic resonance imaging (MRI) measures of brain structure and functional connectivity (fc).

Methods

Thirty-four individuals with migraine and 48 individuals with PPTH attributed to mild TBI were included. All individuals completed questionnaires assessing headache characteristics, mood, sensory hypersensitivities, and cognitive function and underwent brain structural and functional imaging during the same study visit. Clinical features, structural and functional resting-state measures were included as potential variables. Classifiers using ridge logistic regression of principal components were fit on the data. Average accuracy was calculated using leave-one-out cross-validation. Models were fit with and without fc data. The importance of specific variables to the classifier were examined.

Results

With internal variable selection and principal components creation the average accuracy was 72% with fc data and 63.4% without fc data. This classifier with fc data identified individuals with PPTH and individuals with migraine with equal accuracy.

Conclusion

Multivariate models based on clinical characteristics, fc, and brain structural data accurately classify and differentiate PPTH vs. migraine suggesting differences in the neuromechanism and clinical features underlying both headache disorders.

Canonical template tracking: Measuring the activation state of specific neural representations

Multivariate analyses of neural data have become increasingly influential in cognitive neuroscience since they allow to address questions about the representational signatures of neurocognitive phenomena. Here, we describe Canonical Template Tracking: a multivariate approach that employs independent localizer tasks to assess the activation state of specific representations during the execution of cognitive paradigms. We illustrate the benefits of this methodology in characterizing the particular content and format of task-induced representations, comparing it with standard (cross-)decoding and representational similarity analyses. Then, we discuss relevant design decisions for experiments using this analysis approach, focusing on the nature of the localizer tasks from which the canonical templates are derived. We further provide a step-by-step tutorial of this method, stressing the relevant analysis choices for functional magnetic resonance imaging and magneto/electroencephalography data. Importantly, we point out the potential pitfalls linked to canonical template tracking implementation and interpretation of the results, together with recommendations to mitigate them. To conclude, we provide some examples from previous literature that highlight the potential of this analysis to address relevant theoretical questions in cognitive neuroscience.

Predictive modeling of optimism bias using gray matter cortical thickness

People have been shown to be optimistically biased when their future outcome expectancies are assessed. In fact, we display optimism bias (OB) toward our own success when compared to a rival individual's (personal OB [POB]). Similarly, success expectancies for social groups we like reliably exceed those we mention for a rival group (social OB [SOB]). Recent findings suggest the existence of neural underpinnings for OB. Mostly using structural/functional MRI, these findings rely on voxel-based mass-univariate analyses. While these results remain associative in nature, an open question abides whether MRI information can accurately predict OB. In this study, we hence used predictive modelling to forecast the two OBs. The biases were quantified using a validated soccer paradigm, where personal (self versus rival) and social (in-group versus out-group) forms of OB were extracted at the participant level. Later, using gray matter cortical thickness, we predicted POB and SOB via machine-learning. Our model explained 17% variance (R² = 0.17) in individual variability for POB (but not SOB). Key predictors involved the rostral-caudal anterior cingulate cortex, pars orbitalis and entorhinal cortex-areas that have been associated with OB before. We need such predictive models on a larger scale, to help us better understand positive psychology and individual well-being.

External validation of binary machine learning models for pain intensity perception classification from EEG in healthy individuals

Discrimination of pain intensity using machine learning (ML) and electroencephalography (EEG) has significant potential for clinical applications, especially in scenarios where self-report is unsuitable. However, existing research is limited due to a lack of external validation (assessing performance using novel data). We aimed for the first external validation study for pain intensity classification with EEG. Pneumatic pressure stimuli were delivered to the fingernail bed at high and low pain intensities during two independent EEG experiments with healthy participants. Study one (n = 25) was utilised for training and cross-validation. Study two (n = 15) was used for external validation one (identical stimulation parameters to study one) and external validation two (new stimulation parameters). Time-frequency features of peri-stimulus EEG were computed on a single-trial basis for all electrodes. ML training and analysis were performed on a subset of features, identified through feature selection, which were distributed across scalp electrodes and included frontal, central, and parietal regions. Results demonstrated that ML models outperformed chance. The Random Forest (RF) achieved the greatest accuracies of 73.18, 68.32 and 60.42% for cross-validation, external validation one and two, respectively. Importantly, this research is the first to externally validate ML and EEG for the classification of intensity during experimental pain, demonstrating promising performance which generalises to novel samples and paradigms. These findings offer the most rigorous estimates of ML's clinical potential for pain classification.

Temporal pain processing in the primary somatosensory cortex and anterior cingulate cortex

Pain is known to have sensory and affective components. The sensory pain component is encoded by neurons in the primary somatosensory cortex (S1), whereas the emotional or affective pain experience is in large part processed by neural activities in the anterior cingulate cortex (ACC). The timing of how a mechanical or thermal noxious stimulus triggers activation of peripheral pain fibers is well-known. However, the temporal processing of nociceptive inputs in the cortex remains little studied. Here, we took two approaches to examine how nociceptive inputs are processed by the S1 and ACC. We simultaneously recorded local field potentials in both regions, during the application of a brain-computer interface (BCI). First, we compared event related potentials in the S1 and ACC. Next, we used an algorithmic pain decoder enabled by machine-learning to detect the onset of pain which was used during the implementation of the BCI to automatically treat pain. We found that whereas mechanical pain triggered neural activity changes first in the S1, the S1 and ACC processed thermal pain with a reasonably similar time course. These results indicate that the temporal processing of nociceptive information in different regions of the cortex is likely important for the overall pain experience.

Experimental Medicine Approaches in Early-Phase CNS Drug Development

Traditionally, Phase 1 clinical trials were largely conducted in healthy normal volunteers and focused on collection of safety, tolerability, and pharmacokinetic data. However, in the CNS therapeutic area, with more drugs failing in later phase development, Phase 1 trials have undergone an evolution that includes incorporation of novel approaches involving novel study designs, inclusion of biomarkers, and early inclusion of patients to improve the pharmacologic understanding of novel CNS-active compounds early in clinical development with the hope of improving success in later phase pivotal trials. In this chapter, the authors will discuss the changing landscape of Phase 1 clinical trials in CNS, including novel trial methodology, inclusion of pharmacodynamic biomarkers, and experimental medicine approaches to inform early decision-making in clinical development.

[Placebo effects in analgesia : Influence of expectations on the efficacy and tolerability of analgesic treatment]

Expectations of patients influence the perception and neuronal processing of acute and chronic pain and modulate the effectiveness of analgesic treatment. The expectation of treatment is not only the most important determinant of placebo analgesia. Expectations of treatment also influence the efficacy and tolerability of "active" pharmacological and non-pharmacological treatment of pain. Recent insights into the psychological and neurobiological mechanisms underlying the clinically relevant effects of treatment expectations enable and call for the systematic integration and modulation of treatment expectations into analgesic treatment concepts. Such a strategy promises to optimize analgesic treatment and to prevent or reduce the burden of unwanted side effects and the misuse of analgesics, particularly of opioids. This review highlights the current concepts, recent achievements and also challenges and key open research questions.

The rs216009 single-nucleotide polymorphism of the CACNA1C gene is associated with phantom tooth pain

Phantom tooth pain (PTP) is a rare and specific neuropathic pain that occurs after pulpectomy and tooth extraction, but its cause is not understood. We hypothesized that there is a genetic contribution to PTP. The present study focused on the CACNA1C gene, which encodes the α1C subunit of the Cav1.2 L-type Ca2+ channel (LTCC) that has been reported to be associated with neuropathic pain in previous studies. We investigated genetic polymorphisms that contribute to PTP. We statistically examined the association between genetic polymorphisms and PTP vulnerability in 33 patients with PTP and 118 patients without PTP but with pain or dysesthesia in the orofacial region. From within and around the CACNA1C gene, 155 polymorphisms were selected and analyzed for associations with clinical data. We found that the rs216009 single-nucleotide polymorphism (SNP) of the CACNA1C gene in the recessive model was significantly associated with the vulnerability to PTP. Homozygote carriers of the minor C allele of rs216009 had a higher rate of PTP. Nociceptive transmission in neuropathic pain has been reported to involve Ca2+ influx from LTCCs, and the rs216009 polymorphism may be involved in CACNA1C expression, which regulates intracellular Ca2+ levels, leading to the vulnerability to PTP. Furthermore, psychological factors may lead to the development of PTP by modulating the descending pain inhibitory system. Altogether, homozygous C-allele carriers of the rs216009 SNP were more likely to be vulnerable to PTP, possibly through the regulation of intracellular Ca2+ levels and affective pain systems, such as those that mediate fear memory recall.

Resting-state abnormalities in functional connectivity of the default mode network in migraine: A meta-analysis

Migraine-a disabling neurological disorder, imposes a tremendous burden on societies. To reduce the economic and health toll of the disease, insight into its pathophysiological mechanism is key to improving treatment and prevention. Resting-state functional magnetic resonance imaging (rs-fMRI) studies suggest abnormal functional connectivity (FC) within the default mode network (DMN) in migraine patients. This implies that DMN connectivity change may represent a biomarker for migraine. However, the FC abnormalities appear inconsistent which hinders our understanding of the potential neuropathology. Therefore, we performed a meta-analysis of the FC within the DMN in migraine patients in the resting state to identify the common FC abnormalities. With efficient search and selection strategies, nine studies (published before July, 2022) were retrieved, containing 204 migraine patients and 199 healthy subjects. We meta-analyzed the data using the Anisotropic Effect Size version of Signed Differential Mapping (AES-SDM) method. Compared with healthy subjects, migraine patients showed increased connectivity in the right calcarine gyrus, left inferior occipital gyrus, left postcentral gyrus, right cerebellum, right parahippocampal gyrus, and right posterior cingulate gyrus, while decreased connectivity in the right postcentral gyrus, left superior frontal gyrus, right superior occipital gyrus, right orbital inferior frontal gyrus, left middle occipital gyrus, left middle frontal gyrus and left inferior frontal gyrus. These results provide a new perspective for the study of the pathophysiology of migraine and facilitate a more targeted treatment of migraine in the future.