Introduction

It has been estimated that a 10-year delay in the onset of AD may essentially eliminate the disease [1], highlighting the importance of early detection. Although structural MRI is widely used in the assessment of AD-induced morphological changes [2], these changes are known to occur late in the AD trajectory and may not be ideal early biomarkers. Functional and molecular changes precede brain atrophy [3,4] and may be detectable using advanced MRI in the earlier MCI phases, when intervention would prove most beneficial. The goal of this project is to establish advanced MR imaging signatures that are phenotypical of each stage of AD. In this study, arterial spin labeling (ASL) is used to measure cerebral blood flow (CBF), intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) provides measures of perfusion fraction and diffusion, chemical exchange saturation transfer (CEST) is used to probe AD-related molecular species, and susceptibility-weighted imaging (SWI) is sensitive to iron deposition. Each of these metrics was chosen for its sensitivity to a known or suspected pathological change associated AD. We hypothesize that these advanced metrics may provide early biomarkers for incipient MCI or AD, prior to morphological changes.

Methods

Three subject groups were recruited for this cross-sectional study: cognitively normal (CN), MCI, and AD (Table 1). All subjects underwent cognitive testing using the Montreal Cognitive Assessment (MoCA), functional assessment staging (FAST), and clock draw immediately prior to MRI. MRI data were acquired at 3T (Ingenia, Philips). Structural MRI data was obtained using ADNI (Alzheimer's Disease Neuroimaging Initiative) protocols [5], and structural parcellation was performed using FreeSurfer software [6,7]. CBF was measured using a 3D spiral-based pseudo-continuous ASL pulse sequence (TR/TE = 4400/15 ms, voxel size = 2.75 x 2.75 x 6 mm³, 90 mm inferior labeling distance, 1800 ms label duration, and 2000 ms post-labeling delay) [8]. Control and tagged image pairs were acquired, along with an additional proton-density (PD)-weighted imaging volume, to quantify voxel-wise CBF maps. The perfusion-insensitive diffusion coefficient (D) and blood volume fraction (f_p) were obtained using IVIM-DWI (single-shot spin-echo EPI sequence with TR/TE = 6000/50 ms, voxel size = 2.5 mm isotropic, 40 slices, with three orthogonal diffusion-encoding directions and 8 b-values (b = 0, 25, 50, 75, 100, 200, 500, 1000 s/mm²)). The diffusion data were normalized to the b = 0 data and then fit to a bi-exponential model to obtain IVIM parameters [9]. Glutamate (3.0 ppm) and amide (3.5 ppm) proton transfer (APT) were probed using pulsed CEST sequence (3D gradient-echo, TR/TE = 54/8.7 ms, voxel size = 1.5 x 1.5 x 5.0 mm³, 26 slices, 25 ms 1 μT gauss pulse with 48 interleaved offsets). Quantification of GluCEST [10] and APT was calculated using the residual from a Lorentzian fit. SWI was acquired to quantify cerebral microbleeds (3D multi-gradient-echo, TR/TE/ΔTE = 31/7.2/6.2 ms, 4 echoes, voxel size = 1.0 mm isotropic). Minimum intensity projections (minIP) were reconstructed with 10 mm slice thickness. All data were registered to the T₁-weighted image volume using the affine (12 DOF) registration algorithm FLIRT (FSL, FMRIB Centre, Univ. of Oxford) for ROI analysis. Regional metrics were compared using an unpaired t-test. Work is ongoing to acquire longitudinal data in this cohort.

Results / Discussion

Mean (± standard deviation) scores for the MoCA, FAST, and clock-draw are shown in Table 1. Figure 1 illustrates representative imaging examples from each group and imaging method. Across all subjects, regional analysis of structural MRI (Figure 2) revealed significantly reduced cortical thickness and hippocampal volume, while lateral and inferior lateral ventricular volumes were elevated. Figure 3 shows the regional analyses for the advanced MRI biomarkers. Both cerebral cortex and cerebral WM showed lower perfusion for the AD subjects, though these results were not significant. The perfusion-insensitive (IVIM-D) diffusion coefficient showed significant differences in the cerebral cortex, with increased diffusion in the AD subjects (p = 0.002 for CN vs. AD and p = 0.004 for MCI vs. AD). These results may indicate altered microstructural characteristics. GluCEST was reduced in the AD subjects, consistent with prior MR spectroscopy results [11]; additionally, APT-CEST showed lower CEST signal in the AD subjects, suggesting altered protein content in these patients. SWI analysis is currently underway to quantify and characterize cerebral microbleeds in each group.

Conclusions

This study lays the framework for the development of advanced multi-parametric MRI to characterize the neuropathological changes that occur in Alzheimer’s disease. These advanced imaging signatures may be early indicators of incipient AD-related MCI or dementia, when intervention would prove most beneficial.

Acknowledgements

This work was supported by Arizona Alzheimer’s Consortium, Barrow Neurological Foundation, and Philips Healthcare.

References

[1] DeKosky ST, Marek K. Looking Backward to Move Forward: Early Detection of Neurodegenerative Disorders. Science (80- ) 2003;302:830–4. doi:10.1126/science.1090349.
[2] Frisoni GB, Fox NC, Jack Jr. CR, Scheltens P, Thompson PM. The clinical use of structural MRI in Alzheimer disease. Nat Rev Neurol 2010;6:67–77. doi:10.1038/nrneurol.2009.215.
[3] Jack Jr CR, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, et al. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol 2016;9:119–28. doi:10.1016/S1474-4422(09)70299-6.
[4] Li TQ, Wahlund LO. The search for neuroimaging biomarkers of Alzheimer’s disease with advanced MRI techniques. Acta Radiol 2011;52:211–22. doi:10.1258/ar.2010.100053.
[5] Jack Jr. CR, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, et al. The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging 2008;27:685–91. doi:10.1002/jmri.21049.
[6] Dale A, Fischl B, Sereno MI. Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction. Neuroimage 1999;9:179–94.
[7] Fischl B, Sereno MI, Dale A. Cortical Surface-Based Analysis: II: Inflation, Flattening, and a Surface-Based Coordinate System. Neuroimage 1999;9:195–207.
[8] Li Z, Schär M, Wang D, Zwart NR, Madhuranthakam AJ, Karis JP, et al. Arterial spin labeled perfusion imaging using three-dimensional turbo spin echo with a distributed spiral-in/out trajectory. Magn Reson Med 2016;75:266–73. doi:10.1002/mrm.25645.
[9] Meeus EM, Novak J, Withey SB, Zarinabad N, Dehghani H, Peet AC. Evaluation of intravoxel incoherent motion fitting methods in low-perfused tissue. J Magn Reson Imaging 2016. doi:10.1002/jmri.25411.
[10] Haris M, Nath K, Cai K, Singh A, Crescenzi R, Kogan F, et al. Imaging of glutamate neurotransmitter alterations in Alzheimer’s disease. Nmr Biomed 2013;26:386–91. doi:10.1002/nbm.2875.
[11] Rupsingh R, Borrie M, Smith M, Wells JL, Bartha R. Reduced hippocampal glutamate in Alzheimer disease. Neurobiol Aging n.d.;32:802–10. doi:10.1016/j.neurobiolaging.2009.05.002.