Sleep Fragmentation as a Diagnostic Biomarker of Traumatic Brain Injury

Sleep disturbances are among the most prevalent and persistent consequences of traumatic brain injury (TBI), yet they remain underutilized as clinical indicators of injury status. In this perspective, we propose that sleep fragmentation-defined as the frequency of transitions between sleep and wakefulness-represents a functional, scalable, and underrecognized diagnostic biomarker of TBI. Drawing on empirical findings from a mouse model of diffuse TBI, we show that summary measures of sleep fragmentation and duration can reliably distinguish injured from uninjured animals using dimensionality reduction and machine learning techniques. Current biomarkers such as glial fibrillary acidic protein and neurofilament light chain provide valuable insights into structural damage but offer limited information about how injury affects behavior and day-to-day function. Sleep-based metrics, by contrast, reflect neural network integrity and capture ongoing physiological disruption. Critically, these metrics can be collected non-invasively, longitudinally, and in real-world settings using actigraphy, making them a practical complement to blood-based diagnostics that require biological sampling and specialized laboratory infrastructure. Our analysis demonstrates that sleep metrics collected over 48 h post-injury-specifically the number of sleep-wake transitions-carry a strong diagnostic signal. Sleep metrics offer a behaviorally grounded complement aligned with the goals of precision medicine and functional assessment. With further validation, these features may also support monitoring recovery or stratifying injury severity. This perspective highlights sleep fragmentation as a non-invasive diagnostic biomarker for TBI with the potential to enhance individualized monitoring and support early detection efforts in both research and clinical settings.