Using Cerebrospinal Fluid Improves Detection of Individual Brain Atrophy

Background Clinical neuroradiologists routinely look for expansion of CSF spaces to help identify atrophy on patient MRI scans. In contrast, automated methods for identifying atrophy rely on changes in grey matter volume or cortical thickness. It is unclear if evaluating CSF spaces could improve detection of brain atrophy, which may be relevant to improving detection of age- and disease-related atrophy. Methods 3 clinician experts graded atrophy across 7 brain regions from 50 subjects enrolled in the Alzheimer Disease Neuroimaging Initiative (Discovery Cohort, n = 1050 visual ratings) while one expert graded atrophy in an additional 150 subjects (Validation cohort, n = 1050 visual ratings). These subjects included patients with mild cognitive impairment (MCI, n = 72), Alzheimer’s disease (AD, n = 60), and age-matched healthy controls (n = 68), randomly selected from the broader sample. We used an automated approach to detect expansion of CSF spaces and compared it with standard methods for detecting brain atrophy (grey/white matter volume, cortical thickness). We evaluated four metrics of performance: 1) correlation to visually rated atrophy; 2) correlation to clinical symptoms; 3) localization of atrophy most correlated with verbal memory scores; and 4) ability to discriminate between AD, MCI, and controls. Results Atrophy detected by expansion of CSF spaces significantly outperformed existing methods across all performance metrics. 1) CSF-based atrophy correlated with manually assessed atrophy scores (Median Rho = 0.50, p _max = 0.043), and this correlation was stronger than all other methods (max p _FWE = 0.0005). 2) CSF-based atrophy correlated with clinical symptoms (Median Rho = 0.37, IQR 0.34–0.46), and this correlation was stronger than all other methods (max p _FWE = 0.0015). 3) CSF-based atrophy was the only method to localize FWE-significant atrophy covarying with verbal memory scores to the left hippocampus (Rho = 0.57, p _FWE = 0.00302). 4) CSF-based atrophy best differentiated between AD, MCI, and controls (AUC = 0.68, 95% CI 0.61–0.75), and outperformed all other methods (max p _FWE = 0.041). All results were reproducible across discovery and replication cohorts. Conclusion Deriving brain atrophy using CSF can increase sensitivity of atrophy detection, improving alignment with clinical evaluations, explained variance, localization strength, and diagnostic utility.