Highlights Widespread differences in cortical thickness (CT) were observed in patients with low back pain. Changes in CT correlated with self-reported clinical scores of pain and emotion. Changes in resting state fMRI metrics of functional networks. Support vector machines separated low back pain patients from controls with a high performance. Multi-modal biomarkers can be useful when identifying personalized treatments for low back pain.

Chronic low back pain (LBP) is one of the leading causes of disability worldwide. While LBP research has largely focused on the spine, many studies have demonstrated a restructuring of human brain architecture accompanying LBP and other chronic pain states. Brain imaging presents a promising source for discovering noninvasive biomarkers that can improve diagnostic and prognostication outcomes for chronic LBP. This study evaluated graph theory measures derived from brain resting-state functional connectivity (rsFC) as prospective noninvasive biomarkers of LBP. We also proposed and tested a hybrid feature selection method (Enet-subset) that combines Elastic Net and an optimal subset selection method. We collected resting-state functional MRI scans from 24 LBP patients and 27 age-matched healthy controls (HC). We then derived graph-theoretical features and trained a support vector machine (SVM) to classify patient group. The degree centrality (DC), clustering coefficient (CC), and betweenness centrality (BC) were found to be significant predictors of patient group. We achieved an average classification accuracy of 83.1% (p < 0.004) and AUC of 0.937 (p < 0.002), respectively. Similarly, we achieved a sensitivity and specificity of 87.0 and 79.7%. The classification results from this study suggest that graph matrices derived from rsFC can be used as biomarkers of LBP. In addition, our findings suggest that the proposed feature selection method, Enet-subset, might act as a better technique to remove redundant variables and improve the performance of the machine learning classifier.

OBJECTIVE Cervical spondylotic myelopathy (CSM) is the most common cause of chronic spinal cord injury, a significant public health problem. Diffusion tensor imaging (DTI) is a neuroimaging technique widely used to assess CNS tissue pathology and is increasingly used in CSM. However, DTI lacks the needed accuracy, precision, and recall to image pathologies of spinal cord injury as the disease progresses. Thus, the authors used diffusion basis spectrum imaging (DBSI) to delineate white matter injury more accurately in the setting of spinal cord compression. It was hypothesized that the profiles of multiple DBSI metrics can serve as imaging outcome predictors to accurately predict a patient’s response to therapy and his or her long-term prognosis. This hypothesis was tested by using DBSI metrics as input features in a support vector machine (SVM) algorithm. METHODS Fifty patients with CSM and 20 healthy controls were recruited to receive diffusion-weighted MRI examinations. All spinal cord white matter was identified as the region of interest (ROI). DBSI and DTI metrics were extracted from all voxels in the ROI and the median value of each patient was used in analyses. An SVM with optimized hyperparameters was trained using clinical and imaging metrics separately and collectively to predict patient outcomes. Patient outcomes were determined by calculating changes between pre- and postoperative modified Japanese Orthopaedic Association (mJOA) scale scores. RESULTS Accuracy, precision, recall, and F1 score were reported for each SVM iteration. The highest performance was observed when a combination of clinical and DBSI metrics was used to train an SVM. When assessing patient outcomes using mJOA scale scores, the SVM trained with clinical and DBSI metrics achieved accuracy and an area under the curve of 88.1% and 0.95, compared with 66.7% and 0.65, respectively, when clinical and DTI metrics were used together. CONCLUSIONS The accuracy and efficacy of the SVM incorporating clinical and DBSI metrics show promise for clinical applications in predicting patient outcomes. These results suggest that DBSI metrics, along with the clinical presentation, could serve as a surrogate in prognosticating outcomes of patients with CSM.

OBJECTIVES/GOALS: Diffusion basis spectrum imaging (DBSI) allows for detailed evaluation of white matter microstructural changes present in cervical spondylotic myelopathy (CSM). Our goal is to utilize multidimensional clinical and quantitative imaging data to characterize disease severity and predict long-term outcomes in CSM patients undergoing surgery. METHODS/STUDY POPULATION: A single-center prospective cohort study enrolled fifty CSM patients who underwent surgical decompression and twenty healthy controls from 2018-2021. All patients underwent diffusion tensor imaging (DTI), DBSI, and complete clinical evaluations at baseline and 2-years follow-up. Primary outcome measures were the modified Japanese Orthopedic Association score (mild [mJOA 15-17], moderate [mJOA 12-14], severe [mJOA 0-11]) and SF-36 Physical and Mental Component Summaries (PCS and MCS). At 2-years follow-up, improvement was assessed via established MCID thresholds. A supervised machine learning classification model was used to predict treatment outcomes. The highest-performing algorithm was a linear support vector machine. Leave-one-out cross-validation was utilized to test model performance. RESULTS/ANTICIPATED RESULTS: A total of 70 patients – 20 controls, 25 mild, and 25 moderate/severe CSM patients – were enrolled. Baseline clinical and DTI/DBSI measures were significantly different between groups. DBSI Axial and Radial Diffusivity were significantly correlated with baseline mJOA and mJOA recovery, respectively (r=-0.33, p<0.01; r=-0.36, p=0.02). When predicting baseline disease severity (mJOA classification), DTI metrics alone performed with 38.7% accuracy (AUC: 72.2), compared to 95.2% accuracy (AUC: 98.9) with DBSI metrics alone. When predicting improvement after surgery (change in mJOA), clinical variables alone performed with 33.3% accuracy (AUC: 0.40). When combining DTI or DBSI parameters with key clinical covariates, model accuracy improved to 66.7% (AUC: 0.65) and 88.1% (AUC: 0.95) accuracy, respectively. DISCUSSION/SIGNIFICANCE: DBSI metrics correlate with baseline disease severity and outcome measures at 2-years follow-up. Our results suggest that DBSI may serve as a valid non-invasive imaging biomarker for CSM disease severity and potential for postoperative improvement.