Association of MRI-Visible Perivascular Spaces With Longitudinal Cognitive Decline Over a Decade
Clinical Snapshot
PICO Framework
| P — Population | Adults without stroke or dementia (n=750, age 68±9 years, 52% female) from the Vanderbilt Memory and Aging Project |
| I — Intervention | Baseline MRI assessment of perivascular spaces (PVS) volume fraction in basal ganglia |
| C — Comparator | Other cerebral small vessel disease markers (white matter hyperintensities, lacunes, cerebral microbleeds) |
| O — Outcomes | Longitudinal cognitive decline over 11 years across multiple neuropsychological domains including executive function, visuospatial skills, language, and episodic memory |
Bottom Line
This well-conducted prognostic cohort study demonstrates that MRI-visible perivascular spaces in the basal ganglia independently predict longitudinal decline in executive function and visuospatial skills over nearly 5 years, beyond other cerebral small vessel disease markers. The findings support PVS as an emerging neuroimaging biomarker for domain-specific cognitive decline in aging. While the study provides valuable prognostic information, clinical application is currently limited by the need for specialised MRI analysis and lack of validated prediction tools. The single-centre design and absence of external validation limit generalisability. For Australian clinicians, these findings may inform risk stratification in memory clinics where advanced MRI is available, though routine clinical implementation awaits further validation and standardisation of PVS assessment methods.
Key Findings
P Value: All significant associations had confidence intervals excluding zero
Effect Size: Executive function composite: β = -9.51 (independent analysis: β = -7.47); Hooper Visual Organization Test: β = -26.06 (independent: β = -22.11)
Primary Outcome: Longitudinal cognitive decline across multiple neuropsychological domains over mean 4.9±3.1 years follow-up
Nnt Or Sensitivity: Basal ganglia PVS independently predicted executive function and visuospatial decline beyond other SVD markers
Confidence Interval: Executive function: 95% CI -14.33 to -4.68 (independent: -12.84 to -2.10); Hooper: 95% CI -50.53 to -1.59 (independent: -42.38 to -1.85)
Clinical Application
Requires 3T MRI with specialised PVS analysis software, limiting routine clinical application but feasible in specialist memory clinics Relevant for Australian memory clinics and neurologists. MRI accessibility through Medicare for appropriate indications. Aligns with RACGP cognitive assessment guidelines for early detection strategies Older adults without dementia or stroke undergoing cognitive assessment, particularly those with vascular risk factors
Abstract
BACKGROUND AND OBJECTIVES: Cerebral small vessel disease (SVD) is the most common vascular contributor to dementia. SVD markers often coexist, contributing to difficulty assessing their independent contributions to cognitive domains. MRI-visible perivascular spaces (PVSs) are an emerging SVD marker visualized by MRI. We previously showed that basal ganglia PVSs cross-sectionally contribute to worse cognition, independent of other SVD markers. To further characterize the clinical relevance of PVS, we studied its role as a unique SVD marker of longitudinal cognitive decline. METHODS: Participants without stroke or dementia were included in the Vanderbilt Memory and Aging Project, a longitudinal observational cohort study based in Nashville, TN. Participants completed 3T MRI at study entry to measure SVD burden (PVS volume fraction, white matter hyperintensities volume, lacune counts, and cerebral microbleeds counts). PVS volumes were segmented using a deep learning algorithm. Participants underwent comprehensive serial neuropsychological testing over an 11-year follow-up period (mean follow-up = 4.9 ± 3.1 years). Each SVD marker was related to longitudinal neuropsychological performances using a linear mixed-effects model adjusting for age, sex, race/ethnicity, education, baseline cognitive status, apolipoprotein E-ε4 presence, Framingham Stroke Risk Profile, and intracranial volume. Head-to-head comparisons simultaneously tested multiple statistically significant SVD markers. RESULTS: Among participants (n = 750, age 68 ± 9 years, 52% female), higher basal ganglia PVS burden at baseline was associated with worse longitudinal performances in Boston Naming Test (β = -29.63; 95% CI -56.66 to -2.60), Animal Naming (β = -33.09; 95% CI -65.66 to -0.51), Wechsler Adult Intelligence Scale IV Coding (β = -86.36; 95% CI -150.9 to -21.82), executive function composite (β = -9.51; 95% CI -14.33 to -4.68), Hooper Visual Organization Test (β = -26.06; 95% CI -50.53 to -1.59), and episodic memory composite (β = -7.05; 95% CI -11.9 to -2.21). In head-to-head comparisons, basal ganglia PVS remained independent associations with executive function composite (β = -7.47; 95% CI -12.84 to -2.10) and Hooper Visual Organization Test (β = -22.11; 95% CI -42.38 to -1.85). DISCUSSION: Basal ganglia PVS burden independently contributes to worse longitudinal executive function and visuospatial skills independent of other SVD markers, highlighting PVS as an emerging marker of domain-specific cognitive decline in aging. Although causation cannot be established, findings further support PVS as a vascular contributor to deep brain structure damage underlying cognitive decline over time.
References
- 1.Kohno, K., Sun, Y., LeFevre, J. D., Robb, W. H., Jackson, T. B., Liu, D., Vyas, Y., Sweely, B., Pechman, K. R., Shashikumar, N., Peterson, A., Landman, B., Davis, L. T., Hohman, T. J., & Jefferson, A. L. (2026). Association of MRI-visible perivascular spaces with longitudinal cognitive decline over a decade. Neurology. https://doi.org/10.1212/WNL.0000000000214803
Related Research
Sleep medicine reviews
Machine and deep learning in REM sleep behavior disorder: a scoping review and analysis of reporting quality
3 Aug 2026
Neurological research
Federated deep learning model for epilepsy seizure detection using electroencephalogram (EEG) signal
2 Aug 2026
Journal of psychiatric research
Systematic review of machine learning and deep learning models for EEG-based detection of depression.
2 Aug 2026
This content is for educational purposes for healthcare professionals only and does not constitute clinical advice. Clinical decisions should be based on individual patient assessment, current guidelines, and appropriate specialist consultation. Editorial Standards · Privacy Policy · Terms of Service