A multistudy analysis reveals that evoked pain intensity representation is distributed across brain systems

Decoding pain: uncovering the factors that affect the performance of neuroimaging-based pain models

ABSTRACT

Neuroimaging-based pain biomarkers, when combined with machine learning techniques, have demonstrated potential in decoding pain intensity and diagnosing clinical pain conditions. However, a systematic evaluation of how different modeling options affect model performance remains unexplored. This study presents the results from a comprehensive literature survey and benchmark analysis. We conducted a survey of 57 previously published articles that included neuroimaging-based predictive modeling of pain, comparing classification and prediction performance based on the following modeling variables-the levels of data, spatial scales, idiographic vs population models, and sample sizes. The findings revealed a preference for population-level modeling with brain-wide features, aligning with the goal of clinical translation of neuroimaging biomarkers. However, a systematic evaluation of the influence of different modeling options was hindered by a limited number of independent test results. This prompted us to conduct benchmark analyses using a locally collected functional magnetic resonance imaging dataset (N = 124) involving an experimental thermal pain task. The results demonstrated that data levels, spatial scales, and sample sizes significantly impact model performance. Specifically, incorporating more pain-related brain regions, increasing sample sizes, and averaging less data during training and more data during testing improved performance. These findings offer useful guidance for developing neuroimaging-based biomarkers, underscoring the importance of strategic selection of modeling approaches to build better-performing neuroimaging pain biomarkers. However, the generalizability of these findings to clinical pain requires further investigation.

Neuromarkers in addiction: definitions, development strategies, and recent advances

Substance use disorders (SUDs) are the most costly and prevalent psychiatric conditions. Recent calls emphasize a need for biomarkers-measurable, stable indicators of normal and abnormal processes and response to treatment or environmental agents-and, in particular, brain-based neuromarkers that will advance understanding of the neurobiological basis of SUDs and clinical practice. To develop neuromarkers, researchers must be grounded in evidence that a putative marker (i) is sensitive and specific to the psychological phenomenon of interest, (ii) constitutes a predictive model, and (iii) generalizes to novel observations (e.g., through internal cross-validation and external application to novel data). These neuromarkers may be used to index risk of developing SUDs (susceptibility), classify individuals with SUDs (diagnostic), assess risk for progression to more severe pathology (prognostic) or index current severity of pathology (monitoring), detect response to treatment (response), and predict individualized treatment outcomes (predictive). Here, we outline guidelines for developing and assessing neuromarkers, we then review recent advances toward neuromarkers in addiction neuroscience centering our discussion around neuromarkers of craving-a core feature of SUDs. In doing so, we specifically focus on the Neurobiological Craving Signature (NCS), which show great promise for meeting the demand of neuromarkers.

Chemogenetic Modulation of Posterior Insula CaMKIIa Neurons Alters Pain and Thermoregulation

The posterior insular cortex (PIC) is well positioned to perform somatosensory-limbic integration; yet, the function of neuronal subsets within the PIC in processing the sensory and affective dimensions of pain remains unclear. Here, we employ bidirectional chemogenetic modulation to characterize the function of PIC CaMKIIa-expressing excitatory neurons in a comprehensive array of sensory, affective, and thermoregulatory behaviors. Excitatory pyramidal neurons in the PIC were found to be sensitized under inflammatory pain conditions. Chemogenetic activation of excitatory CaMKIIa-expressing PIC neurons in non-injured conditions produced an increase in reflexive and affective pain- and anxiety-like behaviors. Moreover, activation of PIC CaMKIIa-expressing neurons during inflammatory pain conditions exacerbated hyperalgesia and decreased pain tolerance. However, Chemogenetic activation did not alter heat nociception via hot plate latency or body temperature. Conversely, inhibiting CaMKIIa-expressing neurons did not alter either sensory or affective pain-like behaviors in non-injured or under inflammatory pain conditions, but it did decrease body temperature and decreased hot plate latency. Our findings reveal that PIC CaMKIIa-expressing neurons are a critical hub for producing both sensory and affective pain-like behaviors and important for thermoregulatory processing. PERSPECTIVE: The present study reveals that activation of the posterior insula produces hyperalgesia and negative affect, and has a role in thermal tolerance and thermoregulation. These findings highlight the insula as a key player in contributing to the multidimensionality of pain.

Fractal Similarity of Pain Brain Networks

The conscious perception of pain is the result of dynamic interactions of neural activities from local brain regions to distributed brain networks. Mapping out the networks of functional connections between brain regions that form and disperse when an experimental participant received nociceptive stimulations allow to characterize the pattern of network connections related to the pain experience.Although the pattern of intra- and inter-areal connections across the brain are incredibly complex, they appear also largely scale free, with "fractal" connectivity properties reproducing at short and long-time scales. Our results combining intracranial recordings and functional imaging in humans during pain indicate striking similarities in the activity and topological representation of networks at different orders of temporality, with reproduction of patterns of activation from the millisecond to the multisecond range. The connectivity analyzed using graph theory on fMRI data was organized in four sets of brain regions matching those identified through iEEG (i.e., sensorimotor, default mode, central executive, and amygdalo-hippocampal).Here, we discuss similarities in brain network organization at different scales or "orders," in participants as they feel pain. Description of this fractal-like organization may provide clues about how our brain regions work together to create the perception of pain and how pain becomes chronic when its organization is altered.

Direct and Indirect Nociceptive Input from the Trigeminal Dorsal Horn to Pain-Modulating Neurons in the Rostral Ventromedial Medulla

The brain is able to amplify or suppress nociceptive signals by means of descending projections to the spinal and trigeminal dorsal horns from the rostral ventromedial medulla (RVM). Two physiologically defined cell classes within RVM, "ON-cells" and "OFF-cells," respectively facilitate and inhibit nociceptive transmission. However, sensory pathways through which nociceptive input drives changes in RVM cell activity are only now being defined. We recently showed that indirect inputs from the dorsal horn via the parabrachial complex (PB) convey nociceptive information to RVM. The purpose of the present study was to determine whether there are also direct dorsal horn inputs to RVM pain-modulating neurons. We focused on the trigeminal dorsal horn, which conveys sensory input from the face and head, and used a combination of single-cell recording with optogenetic activation and inhibition of projections to RVM and PB from the trigeminal interpolaris-caudalis transition zone (Vi/Vc) in male and female rats. We determined that a direct projection from ventral Vi/Vc to RVM carries nociceptive information to RVM pain-modulating neurons. This projection included a GABAergic component, which could contribute to nociceptive inhibition of OFF-cells. This approach also revealed a parallel, indirect, relay of trigeminal information to RVM via PB. Activation of the indirect pathway through PB produced a more sustained response in RVM compared with activation of the direct projection from Vi/Vc. These data demonstrate that a direct trigeminal output conveys nociceptive information to RVM pain-modulating neurons with a parallel indirect pathway through the parabrachial complex.SIGNIFICANCE STATEMENT Rostral ventromedial medulla (RVM) pain-modulating neurons respond to noxious stimulation, which implies that they receive input from pain-transmission circuits. However, the traditional view has been that there is no direct input to RVM pain-modulating neurons from the dorsal horn, and that nociceptive information is carried by indirect pathways. Indeed, we recently showed that noxious information can reach RVM pain-modulating neurons via the parabrachial complex (PB). Using in vivo electrophysiology and optogenetics, the present study identified a direct relay of nociceptive information from the trigeminal dorsal horn to physiologically identified pain-modulating neurons in RVM. Combined tracing and electrophysiology data revealed that the direct projection includes GABAergic neurons. Direct and indirect pathways may play distinct functional roles in recruiting pain-modulating neurons.

Computational and neural mechanisms of statistical pain learning

Pain invariably changes over time. These fluctuations contain statistical regularities which, in theory, could be learned by the brain to generate expectations and control responses. We demonstrate that humans learn to extract these regularities and explicitly predict the likelihood of forthcoming pain intensities in a manner consistent with optimal Bayesian inference with dynamic update of beliefs. Healthy participants received probabilistic, volatile sequences of low and high-intensity electrical stimuli to the hand during brain fMRI. The inferred frequency of pain correlated with activity in sensorimotor cortical regions and dorsal striatum, whereas the uncertainty of these inferences was encoded in the right superior parietal cortex. Unexpected changes in stimulus frequencies drove the update of internal models by engaging premotor, prefrontal and posterior parietal regions. This study extends our understanding of sensory processing of pain to include the generation of Bayesian internal models of the temporal statistics of pain.

The neural signature of the decision value of future pain

SignificanceWe often willingly experience pain to reach a goal. However, potential pain can also prevent reckless action. How do we consider future pain when deciding on the best course of action? To date, the precise neural mechanisms underlying the valuation of future pain remain unknown. Using functional MRI, we derive a whole-brain signature of the value of future pain capable of predicting participants' choices to accept pain in exchange for a reward. We show that this signature is characterized by a distributed pattern of activity with clear contributions from structures encoding reward and salience, notably the ventral and dorsal striatum. These findings highlight how the brain assigns value to future pain when choosing the best course of action.