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Postconcussive Sleep Problems and Glymphatic Dysfunction Predict Persistent Working Memory Decline
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DOI：10.1177/08977151251380701.png)
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Abstract
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Persistent working memory decline (PWMD) is a common sequela of mild traumatic brain injury (mTBI), yet reliable biomarkers for predicting long-term working memory outcomes remain lacking. The glymphatic system, a brain-wide waste clearance network, plays a crucial role in cognitive recovery. The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index, a noninvasive magnetic resonance imaging (MRI)-based technique, offers a promising approach to evaluate perivascular fluid dynamics—a key component of glymphatic function. However, its role in long-term working memory dysfunction remains underexplored, particularly in the presence of traumatic cerebral microbleeds (CMBs) and poor sleep quality—as measured by Pittsburgh Sleep Quality Index (PSQI)—both of which have been suggested to disrupt glymphatic clearance, exacerbate neurovascular impairment, and contribute to cognitive decline. This study aims to investigate the interplay between CMBs, sleep quality, and perivascular fluid dynamics in predicting PWMD after mTBI. We further assess the feasibility of a machine learning-based approach to enhance individualized working memory outcome prediction. Between September 2015 and October 2022, 3,068 patients presenting with concussion were screened, and 471 met the inclusion criteria for mTBI. A total of 184 patients provided informed consent, and 61 completed both baseline and 1-year follow-up assessments. In addition, 61 demographically matched healthy controls were recruited. Susceptibility-weighted imaging was used to detect CMBs, while perivascular fluid dynamics was assessed using the DTI-ALPS index. Sleep quality was evaluated using the PSQI, and working memory was measured with the Digit Span test at baseline and 1-year post-injury. Mediation analysis was conducted to examine the indirect effects of perivascular fluid dynamics on cognitive outcomes, and a machine learning model incorporating DTI-ALPS, CMBs, sleep quality, and baseline cognitive scores was developed for individualized prediction. CMBs were present in 29.5% of mTBI patients and were associated with significantly lower DTI-ALPS index values (p < 0.001), suggesting compromised perivascular fluid dynamics and glymphatic impairment. Poor sleep quality (PSQI > 8) correlated with lower 1-year Digit Span scores (r = −0.551, p < 0.001), supporting the link between disrupted glymphatic function and cognitive decline. Mediation analysis revealed that the DTI-ALPS index partially mediated the relationship between CMBs and PWMD (Sobel test, p = 0.031). Machine learning-based predictive modeling achieved a high accuracy in forecasting 1-year working memory outcomes (R2 = 0.78). These findings highlight the potential of noninvasive MRI-based assessment of perivascular fluid dynamics as an early biomarker for PWMD. Given the essential role of the glymphatic system in sleep and memory, integrating DTI-ALPS with CMB detection and sleep quality evaluation may enhance prognostic accuracy and inform personalized rehabilitation strategies for mTBI patients.
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