Parenchymal CSF fraction is a measure of brain glymphatic clearance and positively associated with amyloid beta deposition on PET.

INTRODUCTION: Mapping of microscopic changes in the perivascular space (PVS) of the cerebral cortex, beyond magnetic resonance-visible PVS in white matter, may enhance our ability to diagnose Alzheimer's disease (AD) early. METHODS: We used the cerebrospinal fluid (CSF) water fraction (CSFF), a magnetic resonance imaging-based biomarker, to characterize brain parenchymal CSF water, reflecting microscopic PVS in parenchyma. We measured CSFF and amyloid beta (Aß) using 11 C Pittsburgh compound B positron emission tomography to investigate their relationship at both the subject and voxel levels. RESULTS: Our research has demonstrated a positive correlation between the parenchymal CSFF, a non-invasive imaging biomarker indicative of parenchymal glymphatic clearance, and Aß deposition, observed at both individual and voxel-based assessments in the posterior cingulate cortex. DISCUSSION: This study shows that an increased parenchymal CSFF is associated with Aß deposition, suggesting that CSFF could serve as a biomarker for brain glymphatic clearance, which can be used to detect early fluid changes in PVS predisposing individuals to the development of AD. HIGHLIGHTS: Cerebrospinal fluid fraction (CSFF) could be a biomarker of parenchymal perivascular space. CSFF is positively associated with amyloid beta (Aß) deposition at subject level. CSFF in an Aß+ region is higher than in an Aß- region in the posterior cingulate cortex. Correspondence is found between Aß deposition and glymphatic clearance deficits measured by CSFF.

Multimodal assessment of brain fluid clearance is associated with amyloid-beta deposition in humans.

PURPOSE: The present study investigates a multimodal imaging assessment of glymphatic function and its association with brain amyloid-beta deposition. METHODS: Two brain CSF clearance measures (vCSF and DTI-ALPS) were derived from dynamic PET and MR diffusion tensor imaging (DTI) for 50 subjects, 24/50 were Aß positive (Aß+). T1W, T2W, DTI, T2FLAIR, and 11C-PiB and 18F-MK-6240 PET were acquired. Multivariate linear regression models were assessed with both vCSF and DTI-ALPS as independent variables and brain Aß as the dependent variable. Three types of models were evaluated, including the vCSF-only model, the ALPS-only model and the vCSF+ALPS combined model. Models were applied to the whole group, and Aß subgroups. All analyses were controlled for age, gender, and intracranial volume. RESULTS: Sample demographics (N=50) include 20 males and 30 females with a mean age of 69.30 (sd=8.55). Our results show that the combination of vCSF and ALPS associates with Aß deposition (p < 0.05, R2 = 0.575) better than either vCSF (p < 0.05, R2 = 0.431) or ALPS (p < 0.05, R2 = 0.372) alone in the Aß+ group. We observed similar results in whole-group analyses (combined model: p < 0.05, R2 = 0.287; vCSF model: p <0.05, R2 = 0.175; ALPS model: p < 0.05, R2 = 0.196) with less significance. Our data also showed that vCSF has higher correlation (r = -0.548) in subjects with mild Aß deposition and DTI-ALPS has higher correlation (r=-0.451) with severe Aß deposition subjects. CONCLUSION: The regression model with both vCSF and DTI-ALPS is better associated with brain Aß deposition. These two independent brain clearance measures may better explain the variation in Aß deposition than either term individually. Our results suggest that vCSF and DTI-ALPS reflect complementary aspects of brain clearance functions.

Association of brain tissue cerebrospinal fluid fraction with age in healthy cognitively normal adults.

Background and purpose: Our objective was to apply multi-compartment T2 relaxometry in cognitively normal individuals aged 20-80 years to study the effect of aging on the parenchymal CSF fraction (CSFF), a potential measure of the subvoxel CSF space. Materials and methods: A total of 60 volunteers (age range, 22-80 years) were enrolled. Voxel-wise maps of short-T2 myelin water fraction (MWF), intermediate-T2 intra/extra-cellular water fraction (IEWF), and long-T2 CSFF were obtained using fast acquisition with spiral trajectory and adiabatic T2prep (FAST-T2) sequence and three-pool non-linear least squares fitting. Multiple linear regression analyses were performed to study the association between age and regional MWF, IEWF, and CSFF measurements, adjusting for sex and region of interest (ROI) volume. ROIs include the cerebral white matter (WM), cerebral cortex, and subcortical deep gray matter (GM). In each model, a quadratic term for age was tested using an ANOVA test. A Spearman's correlation between the normalized lateral ventricle volume, a measure of organ-level CSF space, and the regional CSFF, a measure of tissue-level CSF space, was computed. Results: Regression analyses showed that there was a statistically significant quadratic relationship with age for CSFF in the cortex (p = 0.018), MWF in the cerebral WM (p = 0.033), deep GM (p = 0.017) and cortex (p = 0.029); and IEWF in the deep GM (p = 0.033). There was a statistically highly significant positive linear relationship between age and regional CSFF in the cerebral WM (p < 0.001) and deep GM (p < 0.001). In addition, there was a statistically significant negative linear association between IEWF and age in the cerebral WM (p = 0.017) and cortex (p < 0.001). In the univariate correlation analysis, the normalized lateral ventricle volume correlated with the regional CSFF measurement in the cerebral WM (ρ = 0.64, p < 0.001), cortex (ρ = 0.62, p < 0.001), and deep GM (ρ = 0.66, p < 0.001). Conclusion: Our cross-sectional data demonstrate that brain tissue water in different compartments shows complex age-dependent patterns. Parenchymal CSFF, a measure of subvoxel CSF-like water in the brain tissue, is quadratically associated with age in the cerebral cortex and linearly associated with age in the cerebral deep GM and WM.