A Multi-Scale Computational Framework for the Longitudinal Analysis of Enlarged Perivascular Space Distributions Under Sleep Deprivation

Abstract

The glymphatic system, responsible for metabolic waste clearance in the central nervous system, has emerged as a critical pathway in the study of neurodegeneration. Enlarged perivascular spaces (ePVS), visible on standard MRI, represent one of the few accessible imaging biomarkers of glymphatic activity in humans. However, existing approaches to longitudinal glymphatic assessment have relied on coarse-grained summary metrics or highly localized diffusion-based indices, leaving the fine-grained spatiotemporal dynamics of ePVS distribution largely unexplored. In this work, we analyze a 7T MRI dataset of 30 healthy subjects scanned at three time points: before sleep deprivation (Baseline), immediately following 24 hours of total sleep deprivation, and 72 hours into recovery. Longitudinal ePVS changes are investigated at four biological scales: whole-brain, regional, sub-regional, and single-voxel. In our first specific aim, we show that coarse-grained ePVS burden metrics do not reveal significant cohort-level responses to sleep deprivation. However, projecting subject-level trajectories into a novel transition space uncovers a robust elastic behavior of the glymphatic system: perturbations in ePVS burden induced by sleep deprivation tend to resolve following recovery, across both segmentation modalities and most brain regions. In our second specific aim, we introduce two novel computational frameworks. A mathematically principled SE(3) mid-space registration pipeline is developed and validated, ensuring unbiased alignment of longitudinal scan pairs. Building on this, a Deformation-Based Morphometry pipeline and an Optimal Transport framework produce single-voxel maps of volumetric changes in the brain parenchyma and ePVS mass redistribution respectively, summarized into fingerprint representations. Cross-transition correlation analysis reveals patterns consistent with the elastic glymphatic response and suggests a directional association between parenchymal adaptations during sleep deprivation and ePVS dynamics during recovery. These results demonstrate the potential of spatially resolved longitudinal ePVS analysis for advancing our understanding of glymphatic dynamics.