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Postconcussive Sleep Problems and Glymphatic Dysfunction Predict Persistent Working Memory Decline.

Yi-Tien Li1,2, David Yen-Ting Chen1,3,4, Duen-Pang Kuo1,5

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Summary
This summary is machine-generated.

Mild traumatic brain injury (mTBI) can cause persistent working memory decline. Diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) shows promise as a biomarker, especially when combined with microbleed and sleep data.

Keywords:
cerebral microbleedsmild traumatic brain injuryperivascular fluid dynamicspersistent working-memory declineprognosticationsleep quality

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Area of Science:

  • Neuroscience
  • Radiology
  • Sleep Medicine

Background:

  • Persistent working memory decline (PWMD) is a common outcome after mild traumatic brain injury (mTBI), lacking reliable predictive biomarkers.
  • The glymphatic system, crucial for cognitive recovery, can be impaired by cerebral microbleeds (CMBs) and poor sleep quality, potentially worsening neurovascular function and cognitive decline.
  • Diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) measures perivascular fluid dynamics, a key aspect of glymphatic function, but its role in mTBI-related PWMD, especially with CMBs and sleep issues, is understudied.

Purpose of the Study:

  • To investigate the combined influence of cerebral microbleeds (CMBs), sleep quality, and perivascular fluid dynamics (assessed by DTI-ALPS) on predicting persistent working memory decline (PWMD) after mild traumatic brain injury (mTBI).
  • To evaluate the effectiveness of a machine learning model integrating these factors for individualized prediction of working memory outcomes post-mTBI.

Main Methods:

  • Recruited 471 mTBI patients and 61 controls, assessing CMBs via susceptibility-weighted imaging, perivascular fluid dynamics using the DTI-ALPS index, and sleep quality with the Pittsburgh Sleep Quality Index (PSQI).
  • Working memory was measured using the Digit Span test at baseline and 1-year follow-up.
  • Employed mediation analysis to explore indirect effects and developed a machine learning model incorporating DTI-ALPS, CMBs, PSQI scores, and baseline cognitive data for prediction.

Main Results:

  • Cerebral microbleeds (CMBs) were found in 29.5% of mTBI patients and were associated with significantly lower DTI-ALPS index values (p < 0.001), indicating impaired glymphatic function.
  • Poor sleep quality (PSQI > 8) correlated with lower 1-year Digit Span scores (r = -0.551, p < 0.001), linking sleep disruption to cognitive decline.
  • The DTI-ALPS index partially mediated the relationship between CMBs and PWMD (Sobel test, p = 0.031), and the machine learning model accurately predicted 1-year working memory outcomes (R² = 0.78).

Conclusions:

  • Noninvasive MRI assessment of perivascular fluid dynamics (DTI-ALPS) shows potential as an early biomarker for predicting persistent working memory decline after mTBI.
  • Integrating DTI-ALPS with CMB detection and sleep quality assessment may improve prognostic accuracy for mTBI patients.
  • These findings support personalized rehabilitation strategies by enhancing the understanding of glymphatic system involvement in mTBI recovery, sleep, and memory.