In vivo imaging of tau pathology using multi-parametric quantitative MRI

Abstract

As the number of people diagnosed with Alzheimer's disease (AD) reaches epidemic proportions, there is an urgent need to develop effective treatment strategies to tackle the social and economic costs of this fatal condition. Dozens of candidate therapeutics are currently being tested in clinical trials, and compounds targeting the aberrant accumulation of tau proteins into neurofibrillary tangles (NFTs) are the focus of substantial current interest. Reliable, translatable biomarkers sensitive to both tau pathology and its modulation by treatment along with animal models that faithfully reflect aspects of the human disease are urgently required. Magnetic resonance imaging (MRI) is well established as a valuable tool for monitoring the structural brain changes that accompany AD progression. However the descent into dementia is not defined by macroscopic brain matter loss alone: non-invasive imaging measurements sensitive to protein accumulation, white matter integrity and cerebral haemodynamics probe distinct aspects of AD pathophysiology and may serve as superior biomarkers for assessing drug efficacy. Here we employ a multi-parametric array of five translatable MRI techniques to characterise the in vivo pathophysiological phenotype of the rTg4510 mouse model of tauopathy (structural imaging, diffusion tensor imaging (DTI), arterial spin labelling (ASL), chemical exchange saturation transfer (CEST) and glucose CEST). Tau-induced pathological changes included grey matter atrophy, increased radial diffusivity in the white matter, decreased amide proton transfer and hyperperfusion. We demonstrate that the above markers unambiguously discriminate between the transgenic group and age-matched controls and provide a comprehensive profile of the multifaceted neuropathological processes underlying the rTg4510 model. Furthermore, we show that ASL and DTI techniques offer heightened sensitivity to processes believed to precede detectable structural changes and, as such, provides a platform for the study of disease mechanisms and therapeutic intervention.

Fig. 1

High resolution structural MRI. A–F) Results from structural analysis, showing TBM statistical results through representative coronal and axial slices of the final average image after 20 iterations of NRR (locations indicated on schematic diagram above). Red: TG4510 brains relatively locally smaller than the average; blue: larger. Based upon FDR-corrected t-statistics (q = 0.05), controlling for total intracranial volume. Clusters smaller than 20 voxels were removed. CB: cerebellum; CX: cortex; EC: entorhinal cortex; H: hippocampus; MB: midbrain; OB: olfactory bulbs; TH: thalamus; V: Ventricles. G) Manually segmented hippocampal volumes (WT and TG), boxplots report median, interquartile range and range. H) Comparison of manually segmented hippocampul volume (rTg4510) to PG positive NFT density, where a significant negative correlation was observed (Pearson's correlation coefficient = 0.81, p = 0.01). I) Typical manual grey matter ROIs used to report average ASL, DTI, CEST, GlucoCEST from the cortex (blue), hippocampus (red) and thalamus (green), overlaid on the average image.

Fig. 2

Quantitative MRI measurements that discriminate tau pathology from wildtype controls. 1st column: Box and whisker plots representing the median, interquartile range and range of the MRI parameters across the rTg4510 and WT mice with individual outliers highlighted by a red cross. 1st row: The radial diffusivity in the corpus callosum; 2nd row: The CBF in the frontal cortex; 3rd row: The CEST APT signal in the hippocampus; 4th row: glucoCEST signal in the cortex. 2nd/3rd column: Maps of radial diffusivity, CBF, CEST APT and GlucoCEST in a representative WT (2nd column) and rTg4510 (3rd column) mouse.

Fig. 3

Immunohistochemistry to estimate regional PG-5 positive NFT density. A) Single slice from a representative rTg4510 mouse with staining for PG-5 positive NFTs. Marked regional dependence of NFT density is observable (see inset (B–D) for examples of cortical, hippocampal and thalamic NFT distribution). E) Quantitative regional estimates of NFT density for each of the 17 WT and 9 rTg4510 mice that underwent MRI (8.5 month age).

Fig. 4

Quantitative MRI correlates to histological ranking of PG-5 positive NFT density. Percentage difference (normalised to control) in A) CBF, B) APT ratio C) MD and D) FA as a function of histological ranking of PG-5 positive NFT density. Spearman's non-parametric correlation coefficient was used to investigate a possible correlation of quantitative MRI to histological ranking (p-value and correlation coefficient (CC) reported in figure inset). Boxplots represent median, interquartile range and range of percentage difference in quantitative MR estimates from WT control with individual outliers highlighted by a red cross.

Fig. 5

CBF and MD in the WT and rTg4510 thalamus (region of “low” tau density). ASL CBF (A) and MD (B) estimates in the WT and rTg4510 thalamus (region of “low” tau density). Boxplots represent median, interquartile range and range with individual outliers highlighted by a red cross.

Fig. 6

Diffusion tensor imaging measurements in the white matter (corpus callosum) of rTg4510 and WT controls. DTI measurements in the white matter (corpus callosum) of rTg4510 and WT controls. Boxplots represent median, interquartile range and range with individual outliers highlighted by a red cross.