Recent advances in objectifying pain using neuroimaging techniques

Measurements of pupillary unrest using infrared pupillometry fail to detect changes in pain intensity in patients after surgery: a prospective observational study

PURPOSE

The pupil displays chaotic oscillations, also referred to as pupillary unrest in ambient light (PUAL). As pain has previously been shown to increase pupillary unrest, the quantitative assessment of PUAL has been considered a possible tool to identify and quantify pain. Nevertheless, PUAL is affected by various states, such as vigilance, cognitive load, or emotional arousal, independent of pain. Furthermore, systematically applied opioids are known to reduce PUAL, thus potentially limiting its usefulness to detect pain or changes in pain intensity. To test the hypothesis that PUAL can reliably identify changes in pain intensity in a clinical setting, we measured PUAL in patients experiencing substantial pain relief when regional anesthesia interventions were applied after surgery.

METHODS

We conducted an observational study at an academic surgery centre following institutional review board approval. Eighteen patients with unsatisfactory pain control following surgery underwent regional anesthesia procedures to improve pain control. We used infrared pupillometry to assess pupillary unrest before and after the regional block. We then compared the changes in pupillary unrest with the changes in pain scores (numeric rating scale [NRS], range 0-10).

RESULTS

Eighteen patients received epidural anesthesia (n = 14) or peripheral nerve blocks (n = 4), resulting in improvement of mean (standard deviation [SD]) NRS pain scores from 7.2 (1.7) to 1.9 (1.8) (difference in means, -2.2; 95% confidence interval [CI], -6.3 to -4.1; P < 0.001). Nevertheless, pupillary unrest did not change as pain decreased; the mean (SD) PUAL was 0.113 (0.062) before analgesia and 0.112 (0.068) after analgesia (difference in means, -0.001; 95% CI, -0.018 to 0.015; P = 0.88).

CONCLUSION

In this prospective observational study, pupillometric measurements of pupillary unrest did not identify changes in pain intensity in a postoperative, predominantly opioid-exposed patient population. While the sample size was small, the use of measurements of pupillary unrest to detect and quantify pain has to be questioned.

Diagnosis of Chronic Musculoskeletal Pain by Using Functional Near-Infrared Spectroscopy and Machine Learning

Chronic pain (CP) has been found to cause significant alternations of the brain's structure and function due to changes in pain processing and disrupted cognitive functions, including with respect to the prefrontal cortex (PFC). However, until now, no studies have used a wearable, low-cost neuroimaging tool capable of performing functional near-infrared spectroscopy (fNIRS) to explore the functional alternations of the PFC and thus automatically achieve a clinical diagnosis of CP. In this case-control study, the pain characteristics of 19 chronic pain patients and 32 healthy controls were measured using fNIRS. Functional connectivity (FC), FC in the PFC, and spontaneous brain activity of the PFC were examined in the CP patients and compared to those of healthy controls (HCs). Then, leave-one-out cross-validation and machine learning algorithms were used to automatically achieve a diagnosis corresponding to a CP patient or an HC. The current study found significantly weaker FC, notably higher small-worldness properties of FC, and increased spontaneous brain activity during resting state within the PFC. Additionally, the resting-state fNIRS measurements exhibited excellent performance in identifying the chronic pain patients via supervised machine learning, achieving F1 score of 0.8229 using only seven features. It is expected that potential FC features can be identified, which can thus serve as a neural marker for the detection of CP using machine learning algorithms. Therefore, the present study will open a new avenue for the diagnosis of chronic musculoskeletal pain by using fNIRS and machine learning techniques.

Beta Electroencephalographic Oscillation Is a Potential GABAergic Biomarker of Chronic Peripheral Neuropathic Pain

This preliminary investigation aimed to assess beta (β) oscillation, a marker of the brain GABAergic signaling, as a potential objective pain marker, hence contributing at the same time to the mechanistic approach of pain management. This case–control observational study measured β electroencephalographic (EEG) oscillation in 12 right-handed adult male with chronic neuropathic pain and 10 matched controls (∼55 years). Participants were submitted to clinical evaluation (pain visual analog scale, Hospital Anxiety, and Depression scale) and a 24-min high-density EEG recording (BIOSEMI). Data were analyzed using the EEGlab toolbox (MATLAB), SPSS, and R. The global power spectrum computed within the low (Lβ, 13–20 Hz) and the high (Hβ, 20–30 Hz) β frequency sub-bands was significantly lower in patients than in controls, and accordingly, Lβ was negatively correlated to the pain visual analog scale (R = −0.931, p = 0.007), whereas Hβ correlation was at the edge of significance (R = −0.805; p = 0.053). Patients’ anxiety was correlated to pain intensity (R = 0.755; p = 0.003). Normalization of the low and high β global power spectrum (GPS) to the GPS of the full frequency range, while confirming the significant Lβ power decrease in chronic neuropathic pain patients, vanished the significance of the Hβ decrease, as well as the correlation between Lβ power and pain intensity. Our results suggest that the GABAergic Lβ EEG oscillation is affected by chronic neuropathic pain. Confirming the Lβ GPS decrease and the correlation with pain intensity in larger studies would open new opportunities for the clinical application of gamma-aminobutyric acid-modifying therapies.

The Functional Role of Response Suppression during an Urge to Relieve Pain

Being in the state of having both a strong impulse to act and a simultaneous need to withhold is commonly described as an "urge." Although urges are part of everyday life and also important to several clinical disorders, the components of urge are poorly understood. It has been conjectured that withholding an action during urge involves active response suppression. We tested that idea by designing an urge paradigm that required participants to resist an impulse to press a button and gain relief from heat (one hand was poised to press while the other arm had heat stimulation). We first used paired-pulse TMS over motor cortex (M1) to measure corticospinal excitability of the hand that could press for relief, while participants withheld movement. We observed increased short-interval intracortical inhibition, an index of M1 GABAergic interneuron activity that was maintained across seconds and specific to the task-relevant finger. A second experiment replicated this. We next used EEG to better "image" putative cortical signatures of motor suppression and pain. We found increased sensorimotor beta contralateral to the task-relevant hand while participants withheld the movement during heat. We interpret this as further evidence of a motor suppressive process. Additionally, there was beta desynchronization contralateral to the arm with heat, which could reflect a pain signature. Strikingly, participants who "suppressed" more exhibited less of a putative "pain" response. We speculate that, during urge, a suppressive state may have functional relevance for both resisting a prohibited action and for mitigating discomfort.