An NIH-funded PET/MRI project investigating brain glial activation in knee osteoarthritis. It measures neuroinflammatory markers before and after knee replacement to examine their relationship with recovery, persistent postoperative pain, and differences between patients.
Project abstract
DESCRIPTION (provided by applicant): Knee osteoarthritis (KOA) is one of the most prevalent causes of pain and disability, and its incidence continues to increase as the elderly and obese populations grow. While most KOA patients report reduced pain and improved function after Total Knee Arthroplasty (TKA), approximately 20% of them continue to experience…Read the full abstractCollapse abstract
DESCRIPTION (provided by applicant): Knee osteoarthritis (KOA) is one of the most prevalent causes of pain and disability, and its incidence continues to increase as the elderly and obese populations grow. While most KOA patients report reduced pain and improved function after Total Knee Arthroplasty (TKA), approximately 20% of them continue to experience significant pain and disability even years post-TKA. The factors underlying inter-individual differences in susceptibility to post-TKA pain are largely unknown. In this project, we will measure levels of the translocator protein (TSPO), a protein upregulated in activated glia, in the brains of patients undergoing TKA and evaluate TSPO's role in post-TKA pain. TSPO functions to limit the magnitude of glia-mediated inflammatory responses, thereby promoting the return to pre-injury status and recovery from pain. Thus, interindividual differences in TSPO expression may explain why a small but substantial percentage of OA patients do not fully heal following TKA. We will perform brain scans in 110 KOA patients (pre-surgically, 1-to-2 weeks post-TKA and, in a subset of patients also 12 months post-TKA), and in 25 healthy volunteers (once). All patients will be also evaluated clinically 1 year post-TKA. Brain scans will be performed using integrated Positron Emission Tomography / Magnetic Resonance (PET/MR) imaging and the recently developed [11C]PBR28 radioligand, which binds to TSPO. MR data collected simultaneously to PET data will allow us to perform an MR-based motion correction of the PET data (a novel procedure that significantly improves the fidelity, sensitivity, and specificity of PT data). First, we will test the hypothesis that pre-surgically KOA patients will demonstrate higher [11C]PBR28 brain binding than healthy controls, which will be evidence of KOA-related glial activation. Then we will evaluate the hypothesis that TKA itself leads to an additional increase of [11C]PBR28 brain binding, which will be evidence of TKA-related glial activation. We also hypothesize that at 1-year post-TKA, [11C]PBR28 binding will still be elevated in patients still experiencing significant pain and disability, whereas it will be reduced to the levels of the contrl subjects in recovered patients. Finally, we will test the hypothesis that [11C]PBR28 binding pre- and peri-surgically will predict occurrence of long-term postsurgical pain 1 year after TKA. In particular, given the proposed anti-inflammatory and pain-protective role of TSPO, we will test the hypothesis that low pre-surgical / peri-surgical TSPO levels will predict higher likelihood of developing post-TKA pain. The identification of a role of glia and its modulation in the development and maintenance of persistent pain and pain-related disability following TKA will have important practical implications for the management of post-operative pain, and the development of tailored preventive interventions focused on glial modulation.
Research connections
Publications
2025
Frontiers in neuroinformatics
INTRODUCTION: Neuroinflammation, a pathophysiological process involved in numerous disorders, is typically imaged using [11C]PBR28 (or TSPO) PET. However, this technique is limited by high costs and ionizing radiation, restricting its widespread clinical use. MRI, a more accessible alternative, is commonly used for structural or functional imaging, but when…Read the full abstractCollapse abstract
INTRODUCTION: Neuroinflammation, a pathophysiological process involved in numerous disorders, is typically imaged using [11C]PBR28 (or TSPO) PET. However, this technique is limited by high costs and ionizing radiation, restricting its widespread clinical use. MRI, a more accessible alternative, is commonly used for structural or functional imaging, but when used using traditional approaches has limited sensitivity to specific molecular processes. This study aims to develop a deep learning model to generate TSPO PET images from structural MRI data collected in human subjects.
METHODS: A total of 204 scans, from participants with knee osteoarthritis (n = 15 scanned once, 15 scanned twice, 14 scanned three times), back pain (n = 40 scanned twice, 3 scanned three times), and healthy controls (n = 28, scanned once), underwent simultaneous 3 T MRI and [11C]PBR28 TSPO PET scans. A 3D U-Net model was trained on 80% of these PET-MRI pairs and validated using 5-fold cross-validation. The model's accuracy in reconstructed PET from MRI only was assessed using various intensity and noise metrics.
RESULTS: The model achieved a low voxel-wise mean squared error (0.0033 ± 0.0010) across all folds and a median contrast-to-noise ratio of 0.0640 ± 0.2500 when comparing true to reconstructed PET images. The synthesized PET images accurately replicated the spatial patterns observed in the original PET data. Additionally, the reconstruction accuracy was maintained even after spatial normalization.
DISCUSSION: This study demonstrates that deep learning can accurately synthesize TSPO PET images from conventional, T1-weighted MRI. This approach could enable low-cost, noninvasive neuroinflammation imaging, expanding the clinical applicability of this imaging method.
2025
Brain, behavior, and immunity
Recent evidence suggests that chronic pain patients exhibit elevated brain levels of the neuroinflammation marker 18 kDa translocator protein (TSPO). However, the clinical significance of brain TSPO elevations, and their responses to pain interventions, remain unknown. To explore these questions, we studied patients with knee osteoarthritis (KOA) undergoing…Read the full abstractCollapse abstract
Recent evidence suggests that chronic pain patients exhibit elevated brain levels of the neuroinflammation marker 18 kDa translocator protein (TSPO). However, the clinical significance of brain TSPO elevations, and their responses to pain interventions, remain unknown. To explore these questions, we studied patients with knee osteoarthritis (KOA) undergoing total knee arthroplasty (TKA), a procedure which is curative for most, but carries a relatively high risk of persistent post-surgical pain. Pre-surgical KOA patients (n = 41) and healthy controls (n = 22) underwent brain positron emission tomography/magnetic resonance imaging, using the TSPO radioligand [11C]PBR28. A subset of KOA patients (n = 27) returned for a second scan one-year post-TKA. When compared groups, pre-surgical KOA patients exhibited widespread [11C]PBR28 PET signal elevations (Standardized Uptake Value Ratio), with pituitary uptake positively correlating with knee pain severity (rho = 0.51; p = 0.003). A voxel-wise paired t-test revealed that while most brain regions showed no change post-surgery, the [11C]PBR28 PET signal significantly decreased in the thalamus and caudate, reaching control levels. Additionally, a Support Vector Machine model based on pre-surgical imaging, clinical, and demographic features, achieved a correlation of rho = 0.487 (p = 0.001) between the predicted and actual pain improvement. Top predictive features included [11C]PBR28 uptake in the pituitary gland, cuneal cortex, amygdala and other regions. This study suggests that neuroinflammation 1) is widespread in KOA and, in some regions, 2) is linked to pain severity, 3) undergoes normalization following TKA, and 4) can predict post-surgical TKA outcomes. Understanding the neuroinflammatory mechanisms in KOA and post-surgical pain may guide targeted interventions and improve patient outcomes.