BACKGROUND CONTEXT: For chronic low back pain, the causal mechanisms between pathological features from imaging and patient symptoms are unclear. For instance, disc herniations can often be present without symptoms. There remains a need for improved knowledge of the pathophysiological mechanisms that explore spinal tissue damage and clinical manifestations of pain and disability. Spaceflight and astronaut health provides a rare opportunity to study potential low back pain mechanisms longitudinally. Spaceflight disrupts diurnal loading on the spine and several lines of evidence indicate that astronauts are at a heightened risk for low back pain and disc herniation following spaceflight. PURPOSE: To examine the relationship between prolonged exposure to microgravity and the elevated incidence of postflight disc herniation, we conducted a longitudinal study to track the spinal health of twelve NASA astronauts before and after approximately 6-months in space. We hypothesize that the incidence of postflight disc herniation and low back complaints associates with spaceflight-included muscle atrophy and pre-existing spinal pathology. STUDY DESIGN: This is a prospective longitudinal study. PATIENT SAMPLE: Our sample included a cohort of twelve astronaut crewmembers. OUTCOME MEASURES: From 3T MRI, we quantified disc water content (ms), disc degeneration (Pfirrmann grade), vertebral end plate irregularities, facet arthropathy and/ fluid, high intensity zones, disc herniation, multifidus total cross-sectional area (cm 2 ), multifidus lean muscle cross-sectional area (cm 2 ), and muscle quality/composition (%). From quantitative fluoroscopy we quantified, maximum flexion-extension ROM (˚), maximum lateral bending ROM (˚), and maximum translation (%). Lastly, patient outcomes and clinical notes were used for identifying postflight symptoms associated with disc herniations from 3T MRI. METHODS: Advanced imaging data from 3T MRI were collected at three separate time points in relation to spending 6-months in space: (1) within a year before launch ("pre-flight"), (2) within a FDA device/drug status: Not applicable.

Purpose The paraspinal muscles (PSM) are a key feature potentially related to low back pain (LBP), and their structure and composition can be quantified using MRI. Most commonly, quantifying PSM measures across individual muscles and individual spinal levels renders numerous separate metrics that are analyzed in isolation. However, comprehensive multivariate approaches would be more appropriate for analyzing the PSM within an individual. To establish and test these methods, we hypothesized that multivariate summaries of PSM MRI measures would associate with the presence of LBP symptoms (i.e., pain intensity). Methods We applied hierarchical multiple factor analysis (hMFA), an unsupervised integrative method, to clinical PSM MRI data from unique cohort datasets including a longitudinal cohort of astronauts with pre- and post-spaceflight data and a cohort of chronic LBP subjects and asymptomatic controls. Three specific use cases were investigated: (1) predicting longitudinal changes in pain using combinations of baseline PSM measures; (2) integrating baseline and post-spaceflight MRI to assess longitudinal change in PSM and how it relates to pain; and (3) integrating PSM quality and adjacent spinal pathology between LBP patients and controls. Results Overall, we found distinct complex relationships with pain intensity between particular muscles and spinal levels. Subjects with high asymmetry between left and right lean muscle composition and differences between spinal segments PSM quality and structure are more likely to increase in pain reported outcome after prolonged time in microgravity. Moreover, changes in PSM quality and structure between pre and post-spaceflight relate to increase in pain after prolonged microgravity. Finally, we show how unsupervised hMFA recapitulates previous research on the association of CEP damage and LBP diagnostic. Conclusion Our analysis considers the spine as a multi-segmental unit as opposed to a series of discrete and isolated spine segments. Integrative and multivariate approaches can be used to distill large and complex imaging datasets thereby improving the clinical utility of MRI-based biomarkers, and providing metrics for further analytical goals, including phenotyping.