Detection of EEG-Based Biomarkers of Chronic Low Back Pain

Chronic low back pain (CLBP) is a pervasive disorder affecting up to one-fifth of adults globally and is the single greatest cause of disability worldwide. Despite the high prevalence and detrimental impact of CLBP, its treatments and mechanisms remain largely unclear. Biomarkers that predict symptom progression in CLBP support precision-based treatments and ultimately aid in reducing suffering. Longitudinal brain-based resting-state neuroimaging of patients with CLBP has revealed neural networks that predict pain chronification and its symptom progression. Although early findings suggest that measurements of brain networks can lead to the development of prognostic biomarkers, the predictive ability of these models is strongest for short-term follow-up. Measurements of different neural systems may provide additional benefits with better predictive power. Emotional and cognitive dysfunction is common in CLBP, occurring at the behavioral and cerebral level, presenting a unique opportunity to detect prognostic brain-based biomarkers. Likewise, improvements in electroencephalogram (EEG) neuroimaging strategies have led to increased spatial resolution, enabling researchers to overcome the limitations of classically used neuroimaging modalities (e.g., magnetic resonance imaging [MRI] and functional MRI), such as high cost and limited accessibility. Using longitudinal EEG, this patient-oriented research project will provide a comprehensive neural picture of emotional, cognitive, and resting-state networks in patients with CLBP, which will aid in predicting symptom progression in CLBP. Through this award, the investigators will use modern EEG source analysis strategies to track biomarkers at baseline and 3- and 6-month follow-ups and their covariance with markers for pain and emotional and cognitive dysfunction. In Aim 1, the investigators will identify and characterize differences in resting-state, emotional, and cognitive networks between patients with CLPB and age/sex-matched controls. In Aim 2, the investigators will identify within-subject changes across time and their relationship with clinical symptoms. In Aim 3, as an exploratory aim, the investigators will apply machine- and deep-learning strategies to detect a comprehensive signature of CLBP using EEG features from resting-state, emotional, and cognitive networks.

During this intervention, participants will be asked to not think about anything in particular while EEG is recorded. Resting state will be conducted with either the participants having their eyes open, or eyes closed.

During this intervention, participants will view emotionally charged pictures for a short period of time. Afterwards, participants will be asked to rate their emotional reactions to the pictures. EEG will be recorded during this intervention.

During this intervention, participants will be asked to respond quickly to a visual stimulus with a button press. At times, participants will be asked to inhibit their responses. EEG will be recorded during this intervention.

Pain Intensity changes from baseline as assessed by the PROMIS current, 7 day maximal and 7 day average

Within subjects change in resting state functional connectivity from baseline to each follow up point.

Within subjects change in metrics of fluid and crystallized intelligence from baseline to each follow up point

Inclusion Criteria: Current diagnosis of Chronic Low Back Pain Exclusion Criteria: Current diagnosis of cancer Severe psychiatric conditions Pending personal litigation relating to an injury or receiving workers' compensation benefits Being a non-English speaker.

The investigators will support requests for data sharing in a timely manner. Prior to sharing data, the data will be redacted of all personal identifiers to prevent subject identification.