Neural representations of aversive value encoding in pain catastrophizers

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.

Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies

Monitoring cerebral oxygenation and metabolism, using a combination of invasive and non-invasive sensors, is vital due to frequent disruptions in hemodynamic regulation across various diseases. These sensors generate continuous high-frequency data streams, including intracranial pressure (ICP) and cerebral perfusion pressure (CPP), providing real-time insights into cerebral function. Analyzing these signals is crucial for understanding complex brain processes, identifying subtle patterns, and detecting anomalies. Computational models play an essential role in linking sensor-derived signals to the underlying physiological state of the brain. Multivariate machine learning models have proven particularly effective in this domain, capturing intricate relationships among multiple variables simultaneously and enabling the accurate modeling of cerebral physiologic signals. These models facilitate the development of advanced diagnostic and prognostic tools, promote patient-specific interventions, and improve therapeutic outcomes. Additionally, machine learning models offer great flexibility, allowing different models to be combined synergistically to address complex challenges in sensor-based data analysis. Ensemble learning techniques, which aggregate predictions from diverse models, further enhance predictive accuracy and robustness. This review explores the use of multivariate machine learning models in cerebral physiology as a whole, with an emphasis on sensor-derived signals related to hemodynamics, cerebral oxygenation, metabolism, and other modalities such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) where applicable. It will detail the operational principles, mathematical foundations, and clinical implications of these models, providing a deeper understanding of their significance in monitoring cerebral function.

Understanding mechanisms of depression prevention: study protocol of a randomized cross-over trial to investigate mechanisms of mindfulness and positive fantasizing as intervention techniques for reducing perseverative cognition in remitted depressed individuals

BACKGROUND

Major Depressive Disorder (MDD) is one of the most prevalent psychiatric disorders, and involves high relapse rates in which persistent negative thinking and rumination (i.e., perseverative cognition [PC]) play an important role. Positive fantasizing and mindfulness are common evidence-based psychological interventions that have been shown to effectively reduce PC and subsequent depressive relapse. How the interventions cause changes in PC over time, is unknown, but likely differ between the two. Whereas fantasizing may change the valence of thought content, mindfulness may operate through disengaging from automatic thought patterns. Comparing mechanisms of both interventions in a clinical sample and a non-clinical sample can give insight into the effectivity of interventions for different individuals. The current study aims to 1) test whether momentary psychological and psychophysiological indices of PC are differentially affected by positive fantasizing versus mindfulness-based interventions, 2) test whether the mechanisms of change by which fantasizing and mindfulness affect PC differ between remitted MDD versus never-depressed (ND) individuals, and 3) explore potential moderators of the main effects of the two interventions (i.e., what works for whom).

METHODS

In this cross-over trial of fantasizing versus mindfulness interventions, we will include 50 remitted MDD and 50 ND individuals. Before the start of the measurements, participants complete several individual characteristics. Daily-life diary measures of thoughts and feelings (using an experience sampling method), behavioural measures of spontaneous thoughts (using the Sustained Attention to Response Task), actigraphy, physiological measures (impedance cardiography, electrocardiography, and electroencephalogram), and measures of depressive mood (self-report questionnaires) are performed during the week before (pre-) the interventions and the week during (peri-) the interventions. After a wash-out of at least one month, pre- and peri-intervention measures for the second intervention are repeated.

DISCUSSION

This is the first study integrating self-reports, behavioural-, and physiological measures capturing dynamics at multiple time scales to examine the differential mechanisms of change in PC by psychological interventions in individuals remitted from multiple MDD episodes and ND individuals. Unravelling how therapeutic techniques affect PC in remitted individuals might generate insights that allows development of personalised targeted relapse prevention interventions.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT06145984, November 16, 2023.

The effect of piezocision vs no piezocision on maxillary extraction space closure: A split-mouth, randomized controlled clinical trial

INTRODUCTION

The aim of this 2-arm-parallel, split-mouth trial was to investigate the effects of piezocision compared with no piezocision on maxillary canine distalization and to evaluate patient perceptions on the surgical procedure.

METHODS

Twenty-two participants requiring extractions of maxillary first premolars were recruited from the Department of Orthodontics (Sydney Dental Hospital) waiting list. After leveling and alignment, a minimum of 3 mm space was required for canine retraction. Piezocision cuts distal to the canines were 4 mm long and 3 mm deep into the buccal cortical plate. The canine retraction was initiated on both sides immediately after surgery, with coil springs delivering 150 g of force per side. Random assignment of piezocision or control intervention on the patient's right side was performed (www.randomisation.com) for the random number generation, and allocation concealment was accomplished with opaque, sealed envelopes. Patients were assessed every 6 weeks for coil activation and alginate impressions over 18 weeks. The primary outcome was the amount of tooth movement in mm. Secondary outcomes were canine rotation, anchorage loss measured on scanned dental models, and patient pain levels and perception on piezocision using visual analog scale questionnaires. Blinding was feasible for the dental model measurements.

RESULTS

Twenty patients completed the trial. The treatment × time interaction showed no statistically or clinically significant differences in maxillary extraction space closure (b = -0.02; 95% confidence interval [CI], -0.29 to 0.25; P = 0.89) canine rotation (b = -1.45; 95% CI, -4 to 1.09; P = 0.26) and anchorage loss (b = -0.02; 95% CI, -0.38 to 0.34; P = 0.92). All patients except for one had minimal pain associated with the piezocision surgery but found the procedure tolerable and would recommend it. No harm occurred during the trial.

CONCLUSIONS

Piezocision-assisted maxillary canine distalization was similar to distalization with conventional orthodontics with patients tolerating the procedure.

Sleep spindles as a diagnostic and therapeutic target for chronic pain

Pain is known to disrupt sleep patterns, and disturbances in sleep can further worsen pain symptoms. Sleep spindles occur during slow wave sleep and have established effects on sensory and affective processing in mammals. A number of chronic neuropsychiatric conditions, meanwhile, are known to alter sleep spindle density. The effect of persistent pain on sleep spindle waves, however, remains unknown, and studies of sleep spindles are challenging due to long period of monitoring and data analysis. Utilizing automated sleep spindle detection algorithms built on deep learning, we can monitor the effect of pain states on sleep spindle activity. In this study, we show that in a chronic pain model in rodents, there is a significant decrease in sleep spindle activity compared to controls. Meanwhile, methods to restore sleep spindles are associated with decreased pain symptoms. These results suggest that sleep spindle density correlates with chronic pain and may be both a potential biomarker for chronic pain and a target for neuromodulation therapy.