Context: Following spinal cord injury (SCI), early prediction of future walking ability is difficult, due to factors such as spinal shock, sedation, impending surgery, and secondary long bone fracture. Accurate, objective biomarkers used in the acute stage of SCI would inform individualized patient management and enhance both patient/family expectations and treatment outcomes. Using magnetic resonance imaging (MRI) and specifically a midsagittal T2-weighted image, the amount of tissue bridging (measured as spared spinal cord tissue) shows potential to serve as such a biomarker. Ten participants with incomplete SCI received MRI of the spinal cord. Using the midsagittal T2-weighted image, anterior and posterior tissue bridges were calculated as the distance from cerebrospinal fluid to the damage. Then, the midsagittal tissue bridge ratio was calculated as the sum of anterior and posterior tissue bridges divided by the spinal cord diameter. Each participant also performed a 6-minute walk test, where the total distance walked was measured within six minutes. Findings: The midsagittal tissue bridge ratio measure demonstrated a high level of inter-rater reliability (ICC = 0.90). Midsagittal tissue bridge ratios were significantly related to distance walked in six minutes (R = 0.68, P = 0.03). Conclusion/clinical relevance: We uniquely demonstrated that midsagittal tissue bridge ratios were correlated walking ability. These preliminary findings suggest potential for this measure to be considered a prognostic biomarker of residual walking ability following SCI.

Study design Retrospective study. Objectives To establish the inter-rater reliability in the quantitative evaluation of spinal cord damage following cervical incomplete spinal cord injury (SCI) utilizing magnetic resonance imaging (MRI). MRI was used to perform manual measurements of the cranial and caudal boundaries of edema, edema length, midsagittal tissue bridge ratio, axial damage ratio, and edema volume in 10 participants with cervical incomplete SCI. Setting Academic university setting. Methods Structural MRIs of 10 participants with SCI were collected from Northwestern University's Neuromuscular Imaging and Research Lab. All manual measures were performed using OsiriX (Pixmeo Sarl, Geneva, Switzerland). Intraclass correlation coefficients (ICC) were used to determine inter-rater reliability across seven raters of varying experience.Results High-to-excellent inter-rater reliability was found for all measures. ICC values for cranial/caudal levels of involvement, edema length, midsagittal tissue bridge ratio, axial damage ratio, and edema volume were 0.99, 0.98, 0.90, 0.84, and 0.93, respectively. Conclusions Manual MRI measures of spinal cord damage are reliable between raters. Researchers and clinicians may confidently utilize manual MRI measures to quantify cord damage. Future research to predict functional recovery following SCI and better inform clinical management is warranted.

The purpose of this study is to leverage modern technology (such as mobile or web apps in Beckman et al. (2014)) to enrich epidemiology data and infer the transmission of disease. Homogeneity related research on population level has been intensively studied in previous work. In contrast, we develop hierarchical Graph-Coupled Hidden Markov Models (hGCH-MMs) to simultaneously track the spread of infection in a small cell phone community and capture person-specific infection parameters by leveraging a link prior that incorporates additional covariates. We also reexamine the model evolution of the hGCHMM from simple HMMs and LDA, elucidating additional flexibility and interpretability. Due to the non-conjugacy of sparsely coupled HMMs, we design a new approximate distribution, allowing our approach to be more applicable to other application areas. Additionally, we investigate two common link functions, the beta-exponential prior and sigmoid function, both of which allow the development of a principled Bayesian hierarchical framework for disease transmission. The results of our model allow us to predict the probability of infection for each person on each day, and also to infer personal physical vulnerability and the relevant association with covariates. We demonstrate our approach experimentally on both simulation data and real epidemiological records.