Using behavioral biomarkers to redefine epochs of spontaneous recovery following spinal cord injury

Abstract

ABSTRACTUncovering the intricacies of the recovery trajectory following spinal cord injury (SCI) has remained a critical exploration for researchers and clinicians, fostering the need of innovative approaches to offer insight into the underlying dynamics of this complex phenomenon. Existing methods, such as the Basso Mouse Scale (BMS) and kinematic analyses, have provided valuable insights, yet limitations in their ability to comprehensively capture behavioral nuances call for more sophisticated approaches. This study addresses segregating the intricate trajectory of recovery following SCI into discrete epochs through the use of behavioral biomarkers. Leveraging a machine learning-driven video analysis technique known as Motion Sequencing (MoSeq), we identified distinct behavioral modules, elucidating shared patterns across diverse injury severities. Our analysis highlights the correlation between these behavioral biomarkers and established recovery metrics, such as BMS criteria and histological markers. Importantly, behavioral biomarkers enabled for deeper understanding of mouse behavior, capturing nuanced features often overlooked by traditional measures. These findings underscore the potential of behavioral biomarkers in characterizing discrete recovery epochs and signatures at the transition from one phase to the next.