1164 Correlation of Structural Brain Imaging and Spectral Sleep Fragmentation in a Large Polysomnography Cohort

Abstract Introduction Sleep physiology and brain health are deeply interconnected. Fragmented sleep has been implicated in cognitive decline, neuroinflammation, and hippocampal vulnerability. However, studies linking quantitative structural MRI to polysomnography (PSG) metrics remain limited. We leveraged a PSG foundation model and a novel measure of spectral sleep fragmentation (SSF) to test whether PSG-derived measures are associated with MRI markers of neurodegeneration. Methods We fine-tuned a foundation model on 10,000 clinical PSG recordings from the Cleveland Clinic (Jan 2012 to Dec 2022). Latent embeddings were aggregated at the patient level and clustered using k-means to define five previously validated PSG risk-groups (RG1-RG5) associated with distinct clinical outcomes. The SSF metric, a frequency-domain metric computed from the spectral representation of the sleep hypnogram, emerged as the most discriminative feature for separating clusters after age. To investigate structural brain correlates, we identified patients with clinical brain MRI analyzed using the automatic software NeuroQuant, which provides structured volumetric measures of hippocampal and ventricular anatomy. We constructed a composite neurodegeneration index (CNI) by combining hippocampal and inferior-lateral ventricular percentiles into z-scores, with higher values indicating greater neurodegenerative change. After filtering for subjects with valid SSF, risk-group assignment, and MRI data, we computed Pearson correlations between SSF and the composite index overall and within each risk group. Results We analyzed 271 MRI reports (Age: 65.7±11.9, Male: 51.4%, BMI: 29.8±6.7). In the overall sample, higher SSF was modestly associated with greater neurodegenerative change (n=271, r=0.19, p=0.002). Stratified analyses per group revealed heterogeneous associations between SSF and CNI across risk-groups. Correlations were small and not significant in RG1-RG4 (RG1:n=89, r=0.17,p=0.10; RG2:n=40,r=0.15,p=0.35; RG3:n=89,r=0.12,p=0.24; RG4:n=39,r=0.02,p=0.90). In contrast, RG5, the highest-risk phenotype, showed a strong positive association between SSF and the neurodegeneration index (n=14, r=0.67, p=0.009), indicating that greater sleep fragmentation in this group tracked with more pronounced hippocampal-ventricular structural changes. Conclusion Spectral sleep fragmentation was associated with a composite index of hippocampal atrophy and ventricular enlargement, with the strongest relationship observed in the highest-risk PSG phenotype. These findings suggest that sleep fragmentation may serve as a relevant marker of neurodegenerative vulnerability, particularly among patients at the highest risk of adverse clinical outcomes. Support (if any)