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

Abstract

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 Aβ positive (Aβ+). 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 Aβ 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 Aβ 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 Aβ 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 Aβ+ 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 Aβ deposition and DTI-ALPS has higher correlation (r=-0.451) with severe Aβ deposition subjects.

Conclusion: The regression model with both vCSF and DTI-ALPS is better associated with brain Aβ deposition. These two independent brain clearance measures may better explain the variation in Aβ deposition than either term individually. Our results suggest that vCSF and DTI-ALPS reflect complementary aspects of brain clearance functions.

Keywords: (11)C-PiB PET; (18)F-MK-6240 PET; Alzheimer's disease; Brain clearance; CSF turnover rate vCSF; DTI-ALPS; Multimodal analysis.

Figure 1

Flowchart of participants finalization.

Figure 2

Illustration of dynamic ¹⁸F-MK6240 in the brain and formulation for calculation of vCSF using TAC in lateral ventricle and area under curve (AUC) of whole brain (WB) TAC.

Figure 3

Illustration of ROIs on (A) color-coded FA for DTI-ALPS, white matter fiber directions and (B) the medullary veins on SWI.

Figure 4

The positive correlation between DTI-ALPS and vCSF. The correlation between them is the strongest in the Aβ+ group.

Figure 5

In whole group, partial regression plot of significant variables versus Aβ deposit in AD mask in vCSF+ALPS model. (A) vCSF; (B) ALPS; and (C) ALPS*vCSF.

Figure 6

In Aβ positive group, partial regression plot of significant variables versus Aβ deposit in AD mask in vCSF+ALPS model. (A) vCSF; (B) ALPS; (C) ALPS*vCSF; and (D) Sex.

Figure 7

In Aβ negative group, partial regression plot of significant variable (Age) versus Aβ deposit measured in AD mask in (A) vCSF+ALPS model, (B) vCSF model; and (C) ALPS model.