ObjectiveThis study aims to investigate the associations of glymphatic system with the presence, severity and neuroimaging phenotypes of cerebral small vessel disease (CSVD) in a community-based population.MethodThis report included 2219 community-dwelling people aged 50–75 years who participated in the PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events cohort. The diffusivity along perivascular spaces based on diffusion tensor imaging (DTI-ALPS index) was measured to assess glymphatic pathway. The presence and severity of CSVD were estimated using a CSVD score (points from 0 to 4) and a modified CSVD score (points from 0 to 4), which were driven by 4 neuroimaging features of CSVD, including white matter hyperintensity (WMH), enlarged perivascular spaces (EPVS), lacunes, cerebral microbleeds. Brain atrophy (BA) was also evaluated. Binary or ordinal logistic regression analyses were carried out to investigate the relationships of DTI-ALPS index with CSVD.ResultThe mean age was 61.3 (SD 6.6) years, and 1019 (45.9%) participants were men. The average DTI-ALPS index was 1.67±0.14. Individuals in the first quartile (Q1) of the DTI-ALPS index had higher risks of the presence of CSVD (OR 1.77, 95% CI 1.33 to 2.35, p<0.001), modified presence of CSVD (odds ratio (OR) 1.80, 95% CI 1.38 to 2.34, p<0.001), total burden of CSVD (common OR (cOR) 1.89, 95% CI 1.43 to 2.49, p<0.001) and modified total burden of CSVD (cOR 1.95, 95% CI 1.51 to 2.50, p<0.001) compared with those in the fourth quartile (Q4). Additionally, individuals in Q1 of the DTI-ALPS index had increased risks of WMH burden, modified WMH burden, lacunes, basal ganglia-EPVS and BA (all p<0.05).ConclusionA lower DTI-ALPS index underlay the presence, severity and typical neuroimaging markers of CSVD, implying that glymphatic impairment may interact with CSVD-related pathology in the general ageing population.Trial registration numberNCT03178448.

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Ischemic strokes (IS) and transient ischemic attacks (TIA) account for approximately 80% of all strokes and are leading causes of death worldwide. Assessing the risk of recurrence or functional impairment in IS and TIA patients is essential to both acute phase treatment and secondary prevention. Current risk prediction systems that rely on clinical parameters alone without leveraging imaging data have only modest performance. Herein, a deep learning‐based risk prediction system (RPS) is developed to predict the probability of stroke recurrence or disability (i.e., deep‐learning stroke recurrence risk score, SRR score). Then, Kaplan–Meier analysis to evaluate the ability of SRR score to stratify patients at stroke recurrence risk is discussed. Using 15 166 Third China National Stroke Registry (CNSR‐III) cases, the RPS's receiver operating characteristic curve (AUC) values of 0.850 for 14 day TIA recurrence prediction and 0.837 for 3 month IS disability prediction are used. Among patients deemed high risk by SRR score, 22.9% and 24.4% of individuals with TIA and IS respectively have stroke recurrence within 1 year, which are significantly higher than the rates in low‐risk individuals. Deep learning‐based RPS can outperform conventional risk scores and has the potential to assist accurate prognostication in stroke patients to optimize management.

The nondemented old–old over the age of 80 comprise a rapidly increasing population group; they can be regarded as exemplars of successful aging. However, our current understanding of successful aging in advanced age and its neural underpinnings is limited. In this study, we measured the microstructural and network-based topological properties of brain white matter using diffusion-weighted imaging scans of 419 community-dwelling nondemented older participants. The participants were further divided into 230 young–old (between 72 and 79, mean = 76.25 ± 2.00) and 219 old–old (between 80 and 92, mean = 83.98 ± 2.97). Results showed that white matter connectivity in microstructure and brain networks significantly declined with increased age and that the declined rates were faster in the old–old compared with young–old. Mediation models indicated that cognitive decline was in part through the age effect on the white matter connectivity in the old–old but not in the young–old. Machine learning predictive models further supported the crucial role of declines in white matter connectivity as a neural substrate of cognitive aging in the nondemented older population. Our findings shed new light on white matter connectivity in the nondemented aging brains and may contribute to uncovering the neural substrates of successful brain aging.