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The ocular glymphatic system supports bidirectional fluid transport along the optic nerve, thereby removes metabolic wastes including amyloid-β. To better understand this biological process, we examined the distributions of intravitreally and intracisternally infused tracers in full-length optic nerves from different age groups of mice. Aging was linked to globally impaired ocular glymphatic fluid transport, similar to what has seen previously in the brain. Aging also reduced the pupillary responsiveness to light stimulation and abolished light-induced facilitation in anterograde ocular glymphatic flow. In contrast to normal aging, in the DBA/2 J model of glaucoma, we found a pathological increase of glymphatic fluid transport to the anterior optic nerve that was associated with dilation of the perivascular spaces. Thus, aging and glaucoma have fundamentally different effects on ocular glymphatic fluid transport. Manipulation of glymphatic fluid transport might therefore present a new target for the treatment of glaucoma.

Deep learning has been demonstrated effective in many neuroimaging applications. However, in many scenarios, the number of imaging sequences capturing information related to small vessel disease lesions is insufficient to support data-driven techniques. Additionally, cohort-based studies may not always have the optimal or essential imaging sequences for accurate lesion detection. Therefore, it is necessary to determine which imaging sequences are crucial for precise detection. This study introduces a deep learning framework to detect enlarged perivascular spaces (ePVS) and aims to find the optimal combination of MRI sequences for deep learning-based quantification. We implemented an effective lightweight U-Net adapted for ePVS detection and comprehensively investigated different combinations of information from SWI, FLAIR, T1-weighted (T1w), and T2-weighted (T2w) MRI sequences. The experimental results showed that T2w MRI is the most important for accurate ePVS detection, and the incorporation of SWI, FLAIR and T1w MRI in the deep neural network had minor improvements in accuracy and resulted in the highest sensitivity and precision (sensitivity = 0.82, precision = 0.83). The proposed method achieved comparable accuracy at a minimal time cost compared to manual reading. The proposed automated pipeline enables robust and time-efficient readings of ePVS from MR scans and demonstrates the importance of T2w MRI for ePVS detection and the potential benefits of using multimodal images. Furthermore, the model provides whole-brain maps of ePVS, enabling a better understanding of their clinical correlates compared to the clinical rating methods within only a couple of brain regions.

ObjectiveTo explore the relationship between heart rate variability (HRV), the brain distribution of enlarged perivascular space (EPVS), and cognitive impairment in patients with EPVS.Materials and methodsThe clinical and imaging data of 199 patients with EPVS were retrospectively analyzed. EPVS load in the basal ganglia (BG) and centrum semiovale (CS) regions were assessed using the Potter’s method. Cognitive function was evaluated using the Montreal Cognitive Assessment Scale. A logistic regression model was used to analyze the relationship between HRV, the brain distribution of EPVS and cognitive function in patients with EPVS. A receiver operating characteristic curve was used to assess the predictive value of HRV for cognitive function in patients with EPVS.ResultsOf the 199 patients, 27 and 42 presented with severe BG-EPVS and cognitive impairment, respectively. Significant differences were observed in the root mean square of successive differences of normal-normal (NN) intervals for period of interest (rMSSD), the percentage of adjacent NN intervals greater than 50 ms (PNN50), and the ratio of low-frequency power (LF) to high-frequency power (HF) between the mild and severe BG-EPVS groups (P < 0.05). Patients who presented with and without cognitive impairment differed significantly in the standard deviation of NN intervals (SDNN), rMSSD, PNN50, total power, LF, and LF/HF (P < 0.05). rMSSD (odds ratio [OR] 0.871, 95% confidence interval [CI] 0.768–0.988) and LF/HF (OR 3.854, 95% CI 1.196–12.419) were independent influencing factors of BG-EPVS, and rMSSD (OR 0.936, 95% CI 0.898–0.976) was an independent influencing factor of cognitive impairment in patients with EPVS. The optimal cut-off point was 0.312, with an area under the curve of 0.795 (95% CI 0.719–0.872) for predicting cognitive impairment in patients with EPVS by rMSSD.ConclusionReduced HRV is involved in the pathophysiological mechanisms of the formation and development of BG-EPVS and is associated with cognitive impairment in patients with EPVS, independent of CS-EPVS. For patients with HRV changes but without autonomic nervous system symptoms, positive intervention may slow the occurrence or progression of EPVS and cognitive impairment in patients with EPVS.

Cerebral small vessel disease (CSVD) is a common cerebrovascular disease and an important cause of gait and balance disorders. Gait and balance disorders can further lead to an increased risk of falls and a decreased quality of life. CSVD can damage gait and balance function by affecting cognitive function or directly disrupting motor pathways, and different CSVD imaging features have different characteristics of gait and balance impairment. In this article, the correlation between different imaging features of sporadic CSVD and gait and balance disorders has been reviewed as follows, which can provide beneficial help for standardized management of CSVD.

(1) Background: This study aimed to investigate the prevalence and risk factors for enlarged perivascular spaces (EPVS) in young adults from a neurology clinic-based cohort (≤45 years old) via unenhanced brain MRI. (2) Methods: A total of 931 young adults from a neurology clinic-based cohort who underwent unenhanced brain MRI between 1 January 2021 and 30 June 2021 were retrospectively included in this study. The EPVS were rated in the centrum semiovale (CSO-EPVS), basal ganglia (BG-EPVS), and midbrain (MB-EPVS) using a visual rating scale. The degrees of the CSO-EPVS, BG-EPVS, and MB-EPVS were all divided by a cutoff value of 1. Demographic factors, vascular risk factors, and symptoms were analyzed using the chi-square test and logistic regression to determine the risk factors of EPVS. (3) Results: The overall prevalence of EPVS was 99.8% (929/931). The CSO-EPVS, BG-EPVS, and MB-EPVS were predominantly scored as 1 (52.1%, 79.1%, and 58.3%, respectively). Logistic regression analysis identified age and hypertension as factors affecting the degrees of CSO-EPVS and BG-EPVS (p < 0.05). Hypertension (p < 0.001) and diabetes (p = 0.014) were revealed to be factors affecting the degree of BG-EPVS. Furthermore, patients with headache (OR = 1.807; p = 0.001) and dizziness (OR = 1.574; p = 0.025) were associated with MB-EPVS. (4) Conclusions: EPVS were frequently found in young adults and could be related to the symptoms. Age, hypertension, and diabetes were the risk factors for the severity of EPVS in the corresponding brain regions.

Abstract Objective We investigated the effect of arterial stiffness on the severity of enlarged perivascular spaces (EPVSs) and cerebral microbleeds (CMBs) at different brain locations. Methods A total of 854 stroke patients underwent both brachial‐ankle pulse wave velocity (baPWV) measurement and brain MRI. The extent of EPVS was separately rated at the levels of the basal ganglia (BG) and centrum semiovale (CS). The CMBs were categorized as strictly lobar CMB and deep CMB. The patients were categorized according to baPWV quartiles, and multivariable logistic regressions were performed to evaluate whether the baPWV increment was independently associated with each cerebral SVD marker at different locations. The odds ratio (OR) with 95% confidence interval (CI) was derived on the reference of the first quartile. Results Severe EPVSs at BG and CS were detected in 243 (28.5%) and 353 patients (41.3%), respectively. The increment of baPWV quartiles was associated with both severe BG EPVS burden (Q4: OR = 2.58, CI = 1.45–4.60) and severe CS EPVS burden (Q4: OR = 2.06, CI = 1.24–3.42). Deep CMBs were found in 259 patients (30.3%), and strictly lobar CMBs were found in 170 patients (19.9%). Multivariable logistic regression model revealed deep CMB was independently associated with the baPWV increment (Q4: OR = 2.52, CI = 1.62–3.94). However, strictly lobar CMB had a neutral relationship with baPWV. Conclusion Increased arterial stiffness is consistently associated with the presence of deep CMB and severe EPVS burden at the BG and CS, suggesting a common pathophysiologic mechanism.

Objective: Cerebral small vessel disease (SVD) is associated with increased mortality, disability and cognitive decline, depression in stroke survivors. This study examined the association between SVD burden, defined by a combination of SVD markers, and health-related quality of life (HRQoL) in acute ischemic stroke.Methods: Patients admitted with acute ischemic stroke of any etiology were prospectively screened between January 2010 to December 2014 and enrolled in the study if they met study entry criteria. HRQoL was evaluated with the 12-item Stroke Specific Quality of Life (SSQoL) at 3 months after the onset of acute ischemic stroke. SVD was ascertained by the presence of any of the SVD markers including lacune, white matter hyperintensities (WMH), cerebral microbleeds (CMB) and enlarged perivascular spaces (EPVS) in the basal ganglia or their combinations on brain magnetic resonance imaging (MRI). The presence of each individual marker scored 1 point and was summed up to generate an ordinal “SVD score” (0–4) capturing total SVD burden. Linear regression was used to determine the associations between SVD burden and HRQoL.Results: Of the743 acute ischemic stroke patients that formed he study sample (mean age: 66.3 ± 10.6 years; 41.7% women), 49.3%, 22.5%, 16.0%, 9.2% and 3.1% had SVD scores of 0, 1, 2, 3 and 4, respectively. After adjusting for demographic, clinical and imaging variables, the SVD score was independently associated with lower overall score of SSQoL (B = −1.39, SE = 0.56, p = 0.01), and its domains of mobility (B = −0.41, SE = 0.10, p < 0.001) and vision (B = −0.12, SE = 0.06, p = 0.03). Acute infract volume (B = −1.44, SE = 0.54, p = 0.01), functional independence (B = 5.69, SE = 0.34, p < 0.001) and anxious (B = −1.13, SE = 0.23, p < 0.001) and depressive symptoms (B = −3.41, SE = 0.22, p < 0.001) were also the significant predictors of the overall score of SSQoL.Conclusion: The brain’s SVD burden predicts lower HRQoL, predominantly in domains of mobility and vision at 3 months after acute ischemic stroke. The evaluation of SVD burden could facilitate developing individual treatment strategies.