International Association for the Study of Pain publications over the 50-year span.

ABSTRACT: The International Association for the Study of Pain (IASP) has a 50-year history of publishing educational and research materials, ranging from traditional print format books, journals, and other informational formats to online and electronic formats. Here we provide a historical overview of IASP publications and reflections from the perspective of 5 former or current Editors-in-Chief.

Prospective Blood Transcriptomics Study in a Motor Vehicle Collision Cohort Identified a Protective Function of the SAMD15 Gene Against Chronic Pain.

Traumatic brain injuries following motor vehicle collisions (MVCs) are ubiquitous. Surprisingly, there are no correlates between concussion impact force and long-term pain outcomes. To study the molecular underpinnings of chronic pain after MVC, we assembled a prospective cohort of 36 subjects that experienced MVC and suffered documented mild traumatic brain injuries. For each participant, a first blood sample was drawn within 72 hours of the collision, then a second one at the 6-month mark. Pain was also assessed at the second blood draw to determine if pain became chronic or resolved. Blood samples enabled transcriptomics analyses for immune cells. At the transcriptome-wide level, we found that Sterile Alpha Motif Domain Containing 15 (SAMD15) mRNA was significantly upregulated with time in subjects who resolved their pain whereas unregulated in those with persistent pain. Using several large publicly available datasets, such as the UK Biobank and the GTeX portal, we then linked elevated SAMD15 gene expression, elevated neutrophils cell counts, and decreased risk for chronic pain to increased dosage of the T allele at SNP rs4903580, situated within SAMD15's gene locus. The causality between the components of our model was established and supported by Mendelian randomization. Overall, our results support the role of SAMD15 as a potential gene effector for neutrophil-dependent chronic pain development. PERSPECTIVE: This article highlights the potential protective role of the SAMD15 gene against chronic pain following a mild traumatic brain injury. The expression of the gene is associated with a SNP rs4903580, which is itself associated with neutrophils counts as well as chronic pain in large genetic studies.

Electroencephalography functional connectivity-A biomarker for painful polyneuropathy.

BACKGROUND AND PURPOSE: Advanced analysis of electroencephalography (EEG) data has become an essential tool in brain research. Based solely on resting state EEG signals, a data-driven, predictive and explanatory approach is presented to discriminate painful from non-painful diabetic polyneuropathy (DPN) patients. METHODS: Three minutes long, 64 electrode resting-state recordings were obtained from 180 DPN patients. The analysis consisted of a mixture of traditional, explanatory and machine learning analyses. First, the 10 functional bivariate connections best differentiating between painful and non-painful patients in each EEG band were identified and the relevant receiver operating characteristic was calculated. Later, those connections were correlated with selected clinical parameters. RESULTS: Predictive analysis indicated that theta and beta bands contain most of the information required for discrimination between painful and non-painful polyneuropathy patients, with area under the receiver operating characteristic curve values of 0.93 for theta and 0.89 for beta bands. Assessing statistical differences between the average magnitude of functional connectivity values and clinical pain parameters revealed that painful DPN patients had significantly higher cortical functional connectivity than non-painful ones (p = 0.008 for theta and p = 0.001 for alpha bands). Moreover, intra-band analysis of individual significant functional connections revealed a positive correlation with average reported pain in the previous 3 months in all frequency bands. CONCLUSIONS: Resting state EEG functional connectivity can serve as a highly accurate biomarker for the presence or absence of pain in DPN patients. This highlights the importance of the brain, in addition to the peripheral lesions, in generating the clinical pain picture. This tool can probably be extended to other pain syndromes.

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.

Resting-state functional connectivity predicts motor cortex stimulation-dependent pain relief in fibromyalgia syndrome patients.

MRI-based resting-state functional connectivity (rsFC) has been shown to predict response to pharmacological and non-pharmacological treatments for chronic pain, but not yet for motor cortex transcranial magnetic stimulation (M1-rTMS). Twenty-seven fibromyalgia syndrome (FMS) patients participated in this double-blind, crossover, and sham-controlled study. Ten daily treatments of 10 Hz M1-rTMS were given over 2 weeks. Before treatment series, patients underwent resting-state fMRI and clinical pain evaluation. Significant pain reduction occurred following active, but not sham, M1-rTMS. The following rsFC patterns predicted reductions in clinical pain intensity after the active treatment: weaker rsFC of the default-mode network with the middle frontal gyrus (r = 0.76, p < 0.001), the executive control network with the rostro-medial prefrontal cortex (r = 0.80, p < 0.001), the thalamus with the middle frontal gyrus (r = 0.82, p < 0.001), and the pregenual anterior cingulate cortex with the inferior parietal lobule (r = 0.79, p < 0.001); and stronger rsFC of the anterior insula with the angular gyrus (r = - 0.81, p < 0.001). The above regions process the attentional and emotional aspects of pain intensity; serve as components of the resting-state networks; are modulated by rTMS; and are altered in FMS. Therefore, we suggest that in FMS, the weaker pre-existing interplay between pain-related brain regions and networks, the larger the pain relief resulting from M1-rTMS.

Brain Connectivity Predicts Chronic Pain in Acute Mild Traumatic Brain Injury.

OBJECTIVES: Previous studies have established the role of the cortico-mesolimbic and descending pain modulation systems in chronic pain prediction. Mild traumatic brain injury (mTBI) is an acute pain model where chronic pain is prevalent and complicated for prediction. In this study, we set out to study whether functional connectivity (FC) of the nucleus accumbens (NAc) and the periaqueductal gray matter (PAG) is predictive of pain chronification in early-acute mTBI. METHODS: To estimate FC, resting-state functional magnetic resonance imaging (fMRI) of 105 participants with mTBI following a motor vehicle collision was acquired within 72 hours post-accident. Participants were classified according to pain ratings provided at 12-months post-collision into chronic pain (head/neck pain ≥30/100, n = 44) and recovery (n = 61) groups, and their FC maps were compared. RESULTS: The chronic pain group exhibited reduced negative FC between NAc and a region within the primary motor cortex corresponding with the expected representation of the area of injury. A complementary pattern was also demonstrated between PAG and the primary somatosensory cortex. PAG and NAc also shared increased FC to the rostral anterior cingulate cortex (rACC) within the recovery group. Brain connectivity further shows high classification accuracy (area under the curve [AUC] = .86) for future chronic pain, when combined with an acute pain intensity report. INTERPRETATION: FC features obtained shortly after mTBI predict its transition to long-term chronic pain, and may reflect an underlying interaction of injury-related primary sensorimotor cortical areas with the mesolimbic and pain modulation systems. Our findings indicate a potential predictive biomarker and highlight targets for future early preventive interventions. ANN NEUROL 2022;92:819-833.

Classification of painful or painless diabetic peripheral neuropathy and identification of the most powerful predictors using machine learning models in large cross-sectional cohorts.

BACKGROUND: To improve the treatment of painful Diabetic Peripheral Neuropathy (DPN) and associated co-morbidities, a better understanding of the pathophysiology and risk factors for painful DPN is required. Using harmonised cohorts (N = 1230) we have built models that classify painful versus painless DPN using quality of life (EQ5D), lifestyle (smoking, alcohol consumption), demographics (age, gender), personality and psychology traits (anxiety, depression, personality traits), biochemical (HbA1c) and clinical variables (BMI, hospital stay and trauma at young age) as predictors. METHODS: The Random Forest, Adaptive Regression Splines and Naive Bayes machine learning models were trained for classifying painful/painless DPN. Their performance was estimated using cross-validation in large cross-sectional cohorts (N = 935) and externally validated in a large population-based cohort (N = 295). Variables were ranked for importance using model specific metrics and marginal effects of predictors were aggregated and assessed at the global level. Model selection was carried out using the Mathews Correlation Coefficient (MCC) and model performance was quantified in the validation set using MCC, the area under the precision/recall curve (AUPRC) and accuracy. RESULTS: Random Forest (MCC = 0.28, AUPRC = 0.76) and Adaptive Regression Splines (MCC = 0.29, AUPRC = 0.77) were the best performing models and showed the smallest reduction in performance between the training and validation dataset. EQ5D index, the 10-item personality dimensions, HbA1c, Depression and Anxiety t-scores, age and Body Mass Index were consistently amongst the most powerful predictors in classifying painful vs painless DPN. CONCLUSIONS: Machine learning models trained on large cross-sectional cohorts were able to accurately classify painful or painless DPN on an independent population-based dataset. Painful DPN is associated with more depression, anxiety and certain personality traits. It is also associated with poorer self-reported quality of life, younger age, poor glucose control and high Body Mass Index (BMI). The models showed good performance in realistic conditions in the presence of missing values and noisy datasets. These models can be used either in the clinical context to assist patient stratification based on the risk of painful DPN or return broad risk categories based on user input. Model's performance and calibration suggest that in both cases they could potentially improve diagnosis and outcomes by changing modifiable factors like BMI and HbA1c control and institute earlier preventive or supportive measures like psychological interventions.

Dispositional and situational personal features and acute post-collision head and neck pain: Double mediation of pain catastrophizing and pain sensitivity.

Pain variability can be partially attributed to psycho-cognitive features involved in its processing. However, accumulating research suggests that simple linear correlation between situational and dispositional factors may not be sufficiently explanatory, with some positing a role for mediating influences. In addition, acute pain processing studies generally focus on a post-operative model with less attention provided to post-traumatic injury. As such, this study aimed to investigate a more comprehensive pain processing model that included direct and indirect associations between acute pain intensity in the head and neck, pain catastrophizing (using pain catastrophizing scale (PCS)), and pain sensitivity (using the pain sensitivity questionnaire (PSQ)), among 239 patients with post-motor vehicle collision pain. The effect of personality traits (using Ten Items Personality Inventory (TIPI)) and emotional status (using Hospital Anxiety and Depression Scale (HADS) and Perceived Stress Scale (PSS)) on that model was examined as well. To this end, three Structural Equation Modeling (SEM) analyses were conducted. Overall, the data had good fit to all the models, with only PSQ found to have a direct correlation with acute pain intensity. The SEM analyses conversely revealed several mediations. Specifically, that: first, PSQ fully mediated the relationship between PCS and pain intensity; second, PCS and PSQ together fully mediated the relationship between conscientiousness (personality trait) and pain intensity; and finally, emotional status had direct and indirect links with PSQ and pain intensity. In conclusion, these models suggest that during the acute post-collision phase, pain sensitivity intermediates between emotional states and personality traits, partially via elevated pain catastrophizing thoughts.

Conditioned pain modulation is more efficient in patients with painful diabetic polyneuropathy than those with nonpainful diabetic polyneuropathy.

ABSTRACT: Endogenous pain modulation, as tested by the conditioned pain modulation (CPM) protocol, is typically less efficient in patients with chronic pain compared with healthy controls. We aimed to assess whether CPM is less efficient in patients with painful diabetic polyneuropathy (DPN) compared with those with nonpainful DPN. Characterization of the differences in central pain processing between these 2 groups might provide a central nervous system explanation to the presence or absence of pain in diabetic neuropathy in addition to the peripheral one. Two hundred seventy-one patients with DPN underwent CPM testing and clinical assessment, including quantitative sensory testing. Two modalities of the test stimuli (heat and pressure) conditioned to cold noxious water were assessed and compared between patients with painful and nonpainful DPN. No significant difference was found between the groups for pressure pain CPM; however, patients with painful DPN demonstrated unexpectedly more efficient CPMHEAT (-7.4 ± 1.0 vs -2.3 ± 1.6; P = 0.008). Efficient CPMHEAT was associated with higher clinical pain experienced in the 24 hours before testing (r = -0.15; P = 0.029) and greater loss of mechanical sensation (r = -0.135; P = 0.042). Moreover, patients who had mechanical hypoesthesia demonstrated more efficient CPMHEAT (P = 0.005). More efficient CPM among patients with painful DPN might result from not only central changes in pain modulation but also from altered sensory messages coming from tested affected body sites. This calls for the use of intact sites for proper assessment of pain modulation in patients with neuropathy.

Clinical Effects of Repetitive Transcranial Magnetic Stimulation of the Motor Cortex Are Associated With Changes in Resting-State Functional Connectivity in Patients With Fibromyalgia Syndrome.

In this double-blinded, sham-controlled, counterbalanced, and crossover study, we investigated the potential neuroplasticity underlying pain relief and daily function improvements following repetitive transcranial magnetic stimulation of the motor cortex (M1-rTMS) in fibromyalgia syndrome (FMS) patients. Specifically, we used magnetic resonance imaging (MRI) to examine changes in brain structural and resting-state functional connectivity (rsFC) that correlated with improvements in FMS symptomology following M1-rTMS. Twenty-seven women with FMS underwent real and sham treatment series, each consisting of 10 daily treatments of 10Hz M1-rTMS over 2 weeks, with a washout period in between. Before and after each series, participants underwent anatomical and resting-state functional MRI scans and questionnaire assessments of FMS-related clinical pain and functional and psychological burdens. The expected reductions in FMS-related symptomology following M1-rTMS occurred with the real treatment only and correlated with rsFC changes in brain areas associated with pain processing and modulation. Specifically, between the ventromedial prefrontal cortex and the M1 (t = -5.54, corrected P = .002), the amygdala and the posterior insula (t = 5.81, corrected P = .044), and the anterior and posterior insula (t = 6.01, corrected P = .029). Neither treatment significantly changed brain structure. Therefore, we provide the first evidence of an association between the acute clinical effects of M1-rTMS in FMS and functional alterations of brain areas that have a significant role in the experience of chronic pain. Structural changes could potentially occur over a more extended treatment period. PERSPECTIVE: We show that the neurophysiological mechanism of the improvement in fibromyalgia symptoms following active, but not sham, rTMS applied to M1 involves changes in resting-state functional connectivity in sensory, affective and cognitive pain processing brain areas, thus substantiating the essence of fibromyalgia syndrome as a treatable brain-based disorder.

Head- and neck-related symptoms post-motor vehicle collision (MVC): Separate entities or two-sides of the same coin?

BACKGROUND AND AIM: Although post-motor vehicle collision (MVC) pain and symptoms are largely convergent among those with mild traumatic brain injury (mTBI) and whiplash associated disorder (WAD), and patients oftentimes report initial neck and head complaints, the clinical picture of mTBI and WAD has been primarily studied as separate conditions which may result in an incomplete clinical picture. As such, this study was conducted to explore the role of pain and post-traumatic psychological features in explaining both head and neck-related symptom variability in a cohort of post-collision patients. This is with the goal of disentangling if contributory factors are uniquely related to each diagnosis, or are shared between the two. METHODS: Patients recruited in the very early acute phase (<72 h) returned for clinical and psychological assessment at 6 months post-accident. In order to determine which factors were unique and which ones were overlapping the same potential contributors: mean head pain, mean neck pain, female gender, number of post-collision painful body areas, PTSD, and depression were included in the regression models for both neck disability index (NDI) and Rivermead post-concussion symptoms questionnaire (RPQ). RESULTS: Of 223 recruited participants, 70 returned for a follow-up visit (age range 18-64, mean(SD) 37.6 (11.9), 29F). This cohort primarily met the criteria for mTBI, but also fulfilled the criteria for whiplash, reinforcing the duality of injury presentation. Correlations existed between the NDI and RPQ scores (Spearman's ρ=0.66, p<0.001), however overlap was only partial. Regression analysis showed that after the removal of area-of-injury pain neck related disability (r = 0.80, p <0.001) was explained solely by number of painful body areas (ß=0.52, p <0.001). In contrast, post-concussion syndrome symptoms (r = 0.86, p<0.001) are influenced by clinical pain, painful body areas (ß=0.31, p = 0.0026), female gender (ß=0.19, p = 0.0053), and psychological factors of depression (ß=0.31, p = 0.0028) and PTSD symptoms (ß=0.36, p = 0.0013). CONCLUSIONS: It seems that while mechanisms of neck- and head-related symptoms in post-collision patients do share a common explanatory feature, of residual body pain, they are not entirely overlapping. In that psychological factors influence post-concussion syndrome symptoms, but not post-whiplash neck disability.

[SMALL FIBER POLYNEUROPATHY IN YOUNG PATIENTS].

INTRODUCTION: Small fiber polyneuropathy (SFPN) is associated with a variety of clinical conditions. Common to these conditions is the deviation from healthy physiological homeostatic balance, which hinders small fiber neurons viability, resulting in their damage. The most common cause for SFPN in the western world is diabetes, followed by a long list of other risk-factors, some are age-related. Accumulating evidence suggests that in young patients a leading cause (up-to 50% of cases) is autoimmune-related. A variety of symptoms can be seen in SFPN. Commonly, first to appear are sensory symptoms in the extremities. Autonomic symptoms can then join, or even be the presenting symptoms. This sensory-autonomic combination can have a dramatic mal-effect on the patient's quality of life. Diagnosis is based primarily on skin biopsy and/or Autonomic-Functional-Testing. Often, in cases where no etiology is identified, EMG is normal and the skin biopsy/autonomic testing is not performed, clinicians tend to incorrectly diagnose a non-organic situation. Correct and preferably early diagnosis is of essence since peripheral fibers can recover if the disease pathophysiological factor is removed, leading to less suffering and improved quality of life of patients.