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1. 66361 TL1 Team Approach to Predicting Response to Spinal Cord Stimulation for Chronic Low Back Pain

   https://doi.org/10.1017/cts.2021.685  

   See, Kyle; Ho, Rachel; Coombes, Stephen; Fang, Ruogu (March 2021, Journal of Clinical and Translational Science)

   ABSTRACT IMPACT: Understanding how spinal cord stimulation works and who it works best for will improve clinical trial efficacy and prevent unnecessary surgeries. OBJECTIVES/GOALS: Spinal cord stimulation (SCS) is an intervention for chronic low back pain where standard interventions fail to provide relief. However, estimates suggest only 58% of patients achieve at least 50% reduction in their pain. There is no non-invasive method for predicting relief provided by SCS. We hypothesize neural activity in the brain can fill this gap. METHODS/STUDY POPULATION: We tested SCS patients at 3 times points: baseline (pre-surgery), at day 7 during the trial period (post-trial), and 6 months after a permanent system had been implanted. At each time point participants completed 10 minutes of eyes closed, resting electroencephalography (EEG) and self-reported their pain. EEG was collected with the ActiveTwo system and a 128-electrode cap. Patients were grouped based on the percentage change of their pain from baseline to the final visit using a median split (super responders > average responders). Spectral density powerbands were extracted from resting EEG to use as input features for machine learning analyses. We used support vector machines to predict response to SCS. RESULTS/ANTICIPATED RESULTS: Baseline and post-trial EEG data predicted SCS response at 6-months with 95.56% and 100% accuracy, respectively. The gamma band had the highest performance in differentiating responders. Post-trial EEG data best differentiated the groups with feature weighted dipoles being more highly localized in sensorimotor cortex. DISCUSSION/SIGNIFICANCE OF FINDINGS: Understanding how SCS works and who it works best for is the long-term objective of our collaborative research program. These data provide an important first step towards this goal. 

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2. Predicting Treatment Outcome in Spinal Cord Stimulation with EEG

   See, Kyle; July, Rachel; Coombes, Stephen; Fang, Ruogu (October 2021, Biomedical Engineering Society Annual Meeting)

   Introduction: Back pain is one of the most common causes of pain in the United States. Spinal cord stimulation (SCS) is an intervention for patients with chronic back pain (CBP). However, SCS decreases pain in only 58% of patients and relies on self-reported pain scores as outcome measures. An SCS trial is temporarily implanted for seven days and helps to determine if a permanent SCS is needed. Patients that have a >50% reduction in pain from the trial stimulator makes them eligible for permanent implantation. However, self-reported measures reveal little on how mechanisms in the brain are altered. Other measurements of pain intensity, onset, medication, disabilities, depression, and anxiety have been used with machine learning to predict outcomes with accuracies <70%. We aim to predict long-term SCS responders at 6-months using baseline resting EEG and machine learning. Materials and Methods: We obtained 10-minutes of resting electroencephalography (EEG) and pain questionnaires from nine participants with CBP at two time points: 1) pre-trial baseline. 2) Six months after SCS permanent implant surgery. Subjects were designated as high or moderate responders based on the amount of pain relief provided by the long-term (post six months) SCS, and pain scored on a scale of 0-10 with 0 being no pain and 10 intolerable. We used the resting EEG from baseline to predict long-term treatment outcome. Resting EEG data was fed through a pipeline for classification and to map dipole sources. EEG signals were preprocessed using the EEGLAB toolbox. Independent component analysis and dipole fitting were used to linearly unmix the signal and to map dipole sources from the brain. Spectral analysis was performed to obtain the frequency distribution of the signal. Each power band, delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-100 Hz), as well as the entire spectrum (1-100 Hz), were used for classification. Furthermore, dipole sources were ranked based on classification feature weights to determine the significance of specific regions in the brain. We used support vector machines to predict pain outcomes. Results and Discussion: We found higher frequency powerbands provide overall classification performance of 88.89%. Differences in power are seen between moderate and high responders in both the frontal and parietal regions for theta, alpha, beta, and the entire spectrum (Fig.1). This can potentially be used to predict patient response to SCS. Conclusions: We found evidence of decreased power in theta, alpha, beta, and entire spectrum in the anterior regions of the parietal cortex and posterior regions of the frontal cortex between moderate and high responders, which can be used for predicting treatment outcomes in long-term pain relief from SCS. Long-term treatment outcome prediction using baseline EEG data has the potential to contribute to decision making in terms of permanent surgery, forgo trial periods, and improve clinical efficiency by beginning to understand the mechanism of action of SCS in the human brain. 

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3. Association between Opioid Dependence and Scale Free Fractal Brain Activity: An EEG Study

   https://doi.org/10.3390/fractalfract7090659  

   Sirpal, Parikshat; Sikora, William; Azizoddin, Desiree; Refai, Hazem; Yang, Yuan (September 2023, Fractal and Fractional)

   Self-similarities at different time scales embedded within a self-organizing neural manifold are well recognized. In this study, we hypothesize that the Hurst fractal dimension (HFD) of the scalp electroencephalographic (EEG) signal reveals statistical differences between chronic pain and opioid use. We test this hypothesis by using EEG resting state signals acquired from a total of 23 human subjects: 14 with chronic pain, 9 with chronic pain taking opioid medications, 5 with chronic pain and not taking opioid medications, and 9 healthy controls. Using the multifractal analysis algorithm, the HFD for full spectrum EEG and EEG frequency band time series was computed for all groups. Our results indicate the HFD varies spatially and temporally across all groups and is of lower magnitude in patients not taking opioids as compared to those taking opioids and healthy controls. A global decrease in HFD was observed with changes in gamma and beta power in the chronic pain group compared to controls and when paired to subject handedness and sex. Our results show the loss of complexity representative of brain wide dysfunction and reduced neural processing can be used as an EEG biomarker for chronic pain and subsequent opioid use. 

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4. COMPASS App: A Patient-centered Physiological based Pain Assessment System

   https://doi.org/10.1177/21695067231192200  

   Zhu, Wenchao; Liu, Chang; Yu, Haoxiang; Guo, Yikang; Xiao, Yan; Lin, Yingzi (September 2023, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Chronic pain patients lack at-home pain assessment and management tools. The existing chronic-pain mobile applications are either solely relying on self-report pain levels or restricted to formal clinical settings. Our app, abbreviated from an NSF-funded project entitled Novel Computational Methods for Continuous Objective Multimodal Pain Assessment Sensing System (COMPASS), is a multi-dimensional pain app that collects physiological signals to predict objective pain levels and trace daily at-home activities by incorporating a daily check-in section. We conducted a usability test with 33 healthy participants under pain conditions. The results provided initial support for the validity of the signals in predicting internalizing pain levels among the participants. With further development and testing, we believe the COMPASS app system has the potential to be used by both patients and clinicians as an additional tool to better assess and manage pain, especially for mobile healthcare applications. 

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5. Differentiating ischemic stroke patients from healthy subjects using a large-scale, retrospective EEG database and machine learning methods

   https://doi.org/10.1016/j.jstrokecerebrovasdis.2024.107714  

   Peterson, William; Ramakrishnan, Nithya; Browder, Krag; Sanossian, Nerses; Nguyen, Peggy; Fink, Ezekiel (June 2024, Journal of Stroke and Cerebrovascular Diseases)

   Objectives: We set out to develop a machine learning model capable of distinguishing patients presenting with ischemic stroke from a healthy cohort of subjects. The model relies on a 3-min resting electroencephalogram (EEG) recording from which features can be computed. Materials and methods: Using a large-scale, retrospective database of EEG recordings and matching clinical reports, we were able to construct a dataset of 1385 healthy subjects and 374 stroke patients. With subjects often producing more than one recording per session, the final dataset consisted of 2401 EEG recordings (63% healthy, 37% stroke). Results: Using a rich set of features encompassing both the spectral and temporal domains, our model yielded an AUC of 0.95, with a sensitivity and specificity of 93% and 86%, respectively. Allowing for multiple recordings per subject in the training set boosted sensitivity by 7%, attributable to a more balanced dataset. Conclusions: Our work demonstrates strong potential for the use of EEG in conjunction with machine learning methods to distinguish stroke patients from healthy subjects. Our approach provides a solution that is not only timely (3-minutes recording time) but also highly precise and accurate (AUC: 0.95). Keywords: Electroencephalogram (EEG); Feature engineering; Ischemic stroke; Large vessel occlusion; Machine learning; Prehospital stroke scale. 

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