Edward Lannon

All Publications

• Beyond pain intensity: Validating single-item pain bothersomeness measures. The journal of pain Edwards, K. A., You, D. S., Lannon, E. W., Dildine, T. C., Darnall, B. D., Mackey, S. C. 2025; 31: 105395

  Abstract

  Chronic pain is a complex and multifaceted health condition, rendering pain intensity alone insufficient for comprehensively understanding the impact of pain. Pain bothersomeness scales have been developed to measure pain's impact on life, however lack adequate validation. Our study examines the validity of two single-item measures (5-point and 11-point response scales) for pain bothersomeness. We collected data from 633 treatment-seeking adults with mixed-etiology chronic pain. Pearson correlations were conducted to examine the relationships between each pain bothersomeness item and 20 general and pain-related functioning measures. ANOVAs examined the ability of pain bothersomeness scores to discriminate between eight groupings based on general and pain-related functioning. Linear regressions analyzed pain bothersomeness as a predictor of 14 measures of general and pain-related functioning three months later. Both pain bothersomeness items correlated significantly with 19 functioning measures and discriminated between eight groupings. Controlling for covariates, both items were consistently predictive of worse pain interference (Total adj r² =.34), pain catastrophizing (Total adj r² =.21-.22), depression (Total adj r² =.25-.26), and satisfaction with social roles (Total adj r² =.13-.16) three months later. Both single-item pain bothersomeness items demonstrated consistent concurrent, discriminant, and predictive validity. These low-burden assessments provide valuable information about patient functioning in clinical settings, suggesting their utility in improving chronic pain care. PERSPECTIVE: Both single-item pain bothersomeness measures demonstrated consistent concurrent and discriminant validity. Both items predicted worse pain interference, pain catastrophizing, depression, and social functioning 3-months later.

  View details for DOI 10.1016/j.jpain.2025.105395

  View details for PubMedID 40228688

• Innovations in acute and chronic pain biomarkers: enhancing diagnosis and personalized therapy. Regional anesthesia and pain medicine Mackey, S., Aghaeepour, N., Gaudilliere, B., Kao, M. C., Kaptan, M., Lannon, E., Pfyffer, D., Weber, K. 2025; 50 (2): 110-120

  Abstract

  Pain affects millions worldwide, posing significant challenges in diagnosis and treatment. Despite advances in understanding pain mechanisms, there remains a critical need for validated biomarkers to enhance diagnosis, prognostication, and personalized therapy. This review synthesizes recent advancements in identifying and validating acute and chronic pain biomarkers, including imaging, molecular, sensory, and neurophysiological approaches. We emphasize the emergence of composite, multimodal strategies that integrate psychosocial factors to improve the precision and applicability of biomarkers in chronic pain management. Neuroimaging techniques like MRI and positron emission tomography provide insights into structural and functional abnormalities related to pain, while electrophysiological methods like electroencepholography and magnetoencepholography assess dysfunctional processing in the pain neuroaxis. Molecular biomarkers, including cytokines, proteomics, and metabolites, offer diagnostic and prognostic potential, though extensive validation is needed. Integrating these biomarkers with psychosocial factors into clinical practice can revolutionize pain management by enabling personalized treatment strategies, improving patient outcomes, and potentially reducing healthcare costs. Future directions include the development of composite biomarker signatures, advances in artificial intelligence, and biomarker signature integration into clinical decision support systems. Rigorous validation and standardization efforts are also necessary to ensure these biomarkers are clinically useful. Large-scale collaborative research will be vital to driving progress in this field and implementing these biomarkers in clinical practice. This comprehensive review highlights the potential of biomarkers to transform acute and chronic pain management, offering hope for improved diagnosis, treatment personalization, and patient outcomes.

  View details for DOI 10.1136/rapm-2024-106030

  View details for PubMedID 39909549

• Blind source separation of event-related potentials using a recurrent neural network. bioRxiv : the preprint server for biology O'Reilly, J. A., Sunthornwiriya-Amon, H., Aparprasith, N., Kittichalao, P., Chairojwong, P., Klai-On, T., Lannon, E. W. 2024

  Abstract

  Event-related potentials (ERPs) are a superposition of electric potential differences generated by neurophysiological activity associated with psychophysical events. Spatiotemporal dissociation of these signal sources can supplement conventional ERP analysis and improve source localization. However, results from established source separation methods applied to ERPs can be challenging to interpret. Hence, we have developed a recurrent neural network (RNN) method for blind source separation. The RNN transforms input step pulse signals representing events into corresponding ERP difference waveforms. Source waveforms are obtained from penultimate layer units and scalp maps are obtained from feed-forward output layer weights that project these source waveforms onto EEG electrode amplitudes. An interpretable, sparse source representation is achieved by incorporating L1 regularization of signals obtained from the penultimate layer of the network during training. This RNN method was applied to four ERP difference waveforms (MMN, N170, N400, P3) from the open-access ERP CORE database, and independent component analysis (ICA) was applied to the same data for comparison. The RNN decomposed these ERPs into eleven spatially and temporally separate sources that were less noisy, tended to be more ERP-specific, and were less similar to each other than ICA-derived sources. The RNN sources also had less ambiguity between source waveform amplitude, scalp potential polarity, and equivalent current dipole orientation than ICA sources. In conclusion, the proposed RNN blind source separation method can be effectively applied to grand-average ERP difference waves and holds promise for further development as a computational model of event-related neural signals.

  View details for DOI 10.1101/2024.04.23.590794

  View details for PubMedID 38712076

  View details for PubMedCentralID PMC11071372

• Assessing Differences in Healthcare Discrimination as a Function of High Impact Chronic Pain and Opioid Use Dildine, T. C., Lannon, E., Mackey, S., You, D. S. CHURCHILL LIVINGSTONE. 2024: 37

  View details for Web of Science ID 001282167300167

• CONSIDERING SOCIOCULTURAL BIASES IN CHRONIC PAIN TREATMENT AND PAIN OUTCOMES Dildine, T., Edwards, K. A., Lannon, E., Darnall, B., Mackey, S. C. OXFORD UNIV PRESS INC. 2024: S127

  View details for Web of Science ID 001271352800255

• Establishing the interpretability and utility of the 4-item BriefPCS. Scientific reports You, D. S., Cook, K. F., Lannon, E. W., Ziadni, M. S., Darnall, B. D., Mackey, S. C. 2023; 13 (1): 21272

  Abstract

  To reduce the patient burden associated with completing the 13-item Pain Catastrophizing Scale (PCS), the 4-item "BriefPCS" was developed. To date, no crosswalk has been developed that associates scores on the BriefPCS with PCS scores. Further, no study has compared the use of BriefPCS and PCS scores in a randomized clinical trial (RCT). We aimed to: (1) establish the interpretability of BriefPCS scores in reference to PCS scores, (2) compare the concurrent validity between the BriefPCS and PCS, and (3) asssess the use of BriefPCS in an RCT. First, we conducted equipercentile linking, created a crosswalk that associated scores of BriefPCS with PCS, and calculated differences between PCS and crosswalked PCS scores. Secondly, we compared Bootstrap correlation coefficients between PCS and self-reported measures of other domains. Lastly, we compared results from an RCT using BriefPCS scores versus PCS scores. Findings indicated that the correlation coefficient estimates with the BriefPCS and PCS scores were not significantly different. BriefPCS and PCS scores had similar ability to detect treatment-related changes. The BriefPCS scores validly, reliably, and accurately distinguish levels of pain catastrophizing. Additionally, the BriefPCS scores are sensitive to changes after behavioral interventions, with less respondent burden compared to the PCS scores.

  View details for DOI 10.1038/s41598-023-48433-6

  View details for PubMedID 38042937

  View details for PubMedCentralID 8369357

• EEG Power Spectrum Predict Concurrent And Future Depression In Chronic Pain Lannon, E., You, D. S., Mackey, S. CHURCHILL LIVINGSTONE. 2023: 76-77

  View details for Web of Science ID 000995432100205

• Exploration of the trait-activation model of pain catastrophizing in Native Americans: results from the Oklahoma Study of Native American pain risk (OK-SNAP). Scandinavian journal of pain Lannon, E. W., Hellman, N., Huber, F. A., Kuhn, B. L., Sturycz, C. A., Palit, S., Payne, M. F., Guereca, Y. M., Toledo, T., Shadlow, J. O., Rhudy, J. L. 2022

  Abstract

  OBJECTIVES: Native Americans (NAs) have the highest prevalence of chronic pain of any racial/ethnic group. This issue has received little attention from the scientific community. One factor that may contribute to racial pain disparities is pain catastrophizing. Pain catastrophizing is a construct related to negative pain outcomes in persons with/without chronic pain. It has been suggested that the relationship between trait catastrophizing and pain is mediated by situation-specific (state) catastrophizing. The present study has 2 aims: (1) to investigate whether state pain catastrophizing mediates the relationship between trait catastrophizing and experimental pain (e.g., cold, ischemic, heat and electric tolerance), and (2) to investigate whether this relationship is stronger for NAs.METHODS: 145 non-Hispanic Whites (NHWs) and 137 NAs completed the study. Bootstrapped indirect effects were calculated for 4 unmoderated and 8 moderated mediation models (4 models with path a moderated and 4 with path b).RESULTS: Consistent with trait-activation theory, significant indirect effects indicated a tendency for trait catastrophizing to be associated with greater state catastrophizing which in turn is associated with reduced pain tolerance during tonic cold (a*b=-0.158) and ischemia stimuli (a*b=-0.126), but not during phasic electric and heat stimuli. Moderation was only noted for the prediction of cold tolerance (path a). Contrary to expectations, the indirect path was stronger for NHWs (a*b for NHW=-.142).CONCLUSIONS: Together, these findings suggest that state catastrophizing mediates the relationship between trait catastrophizing and some measures of pain tolerance but this indirect effect was non-significant for NAs.

  View details for DOI 10.1515/sjpain-2021-0174

  View details for PubMedID 35289511

• Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach. PloS one Lannon, E., Sanchez-Saez, F., Bailey, B., Hellman, N., Kinney, K., Williams, A., Nag, S., Kutcher, M. E., Goodin, B. R., Rao, U., Morris, M. C. 2021; 16 (7): e0255277

  Abstract

  Interpersonal violence (IPV) is highly prevalent in the United States and is a major public health problem. The emergence and/or worsening of chronic pain are known sequelae of IPV; however, not all those who experience IPV develop chronic pain. To mitigate its development, it is critical to identify the factors that are associated with increased risk of pain after IPV. This proof-of-concept study used machine-learning strategies to predict pain severity and interference in 47 young women, ages 18 to 30, who experienced an incident of IPV (i.e., physical and/or sexual assault) within three months of their baseline assessment. Young women are more likely than men to experience IPV and to subsequently develop posttraumatic stress disorder (PTSD) and chronic pain. Women completed a comprehensive assessment of theory-driven cognitive and neurobiological predictors of pain severity and pain-related interference (e.g., pain, coping, disability, psychiatric diagnosis/symptoms, PTSD/trauma, executive function, neuroendocrine, and physiological stress response). Gradient boosting machine models were used to predict symptoms of pain severity and pain-related interference across time (Baseline, 1-,3-,6- follow-up assessments). Models showed excellent predictive performance for pain severity and adequate predictive performance for pain-related interference. This proof-of-concept study suggests that machine-learning approaches are a useful tool for identifying predictors of pain development in survivors of recent IPV. Baseline measures of pain, family life impairment, neuropsychological function, and trauma history were of greatest importance in predicting pain and pain-related interference across a 6-month follow-up period. Present findings support the use of machine-learning techniques in larger studies of post-IPV pain development and highlight theory-driven predictors that could inform the development of targeted early intervention programs. However, these results should be replicated in a larger dataset with lower levels of missing data.

  View details for DOI 10.1371/journal.pone.0255277

  View details for PubMedID 34324550