We propose a multivariate approach for characterizing mouse models of Alzheimer’s disease (AD), which integrates imaging and behavior in a joint analysis. We used manganese enhanced MRI (MEMRI) to identify brain areas associated with reduced performance in a spatial memory task. We quantified genotype differences based on morphometry, T1 weighted (T1W) signal and quantitative susceptibility maps (QSM). We find that the integration of multiple imaging biomarkers is a better predictor of cognitive decline, relative to using single biomarkers in isolation.
Animals. 11 APPSwDI/mNOS2-/- (CVN-AD) models, and 13 mNOS2-/- controls, aged 75±4.38 weeks were implanted with continuous infusion minipumps (Durect Corp, Cupertino, CA, containing 100 µl of 64 µm/µl MnCl2.
Memory testing Over 5 days, mice received 4 trials, during which they swim to find a submerged platform. Behavior was measured using Ethovision (Noldus, Blacksburg, VA), and analyzed by repeated measure analysis of variance (ANOVA) with Greenhouse-Geisser correction.
Imaging A 7 T magnet (Bruker BioSpec 70/20 USR, Billerica, MA) and a quadrature RF cryogenic coil were used to image the brain in vivo at 100 µm resolution. To quantify morphometric changes and manganese uptake we used a T1 weighted RARE sequence with FOV 2x2x1 cm, matrix 200x200x100, NEX=2, BW=100kHz , minTE=10.3 ms, spacing 10.3 ms, TR=150ms , 4 RARE partitions , acquired in 23 min. To calculate quantitative susceptibility maps (QSM) we used a respiratory gated multi echo GRE with FOV 1.92x1.92x0.9; matrix 192x192x90, 8 echoes with 5.5 ms spacing, NEX=1, TE1=3.9 ms; TR=100 ms, flip=30°, BW=62.5kHz, acquired in ~30-40 mins.
Image Processing. Images were bias field corrected (1), skull-stripped (2), and then averaged to create a minimum deformation template (MDT) (3, 4). The deformation fields mapping individuals to the MDT were used for morphometry. T1 weighted images were normalized to individual’s averages to quantify manganese uptake, and estimate neuronal activity. QSM were calculated using a two-step streaking artifact reduction reconstruction (5). For voxel-wise analysis, all images (log Jacobian, normalized T1w RARE, and QSM) were mapped into the MDT, and smoothed with a 200μm kernel. SPM (6) was used, with false discovery rate correction.
Predictive Modelling. We used sparse canonical correlation between imaging and behavior (7). We used a 0.5 anatomical prior initialization weight, 1% sparseness, 100 voxels cluster threshold, with 5 iterations, to predict behavior from imaging data, which were split into training and testing (75:25).
We phenotyped mouse models of AD based jointly on imaging and behavior. We developed imaging protocols (Fig 1) to characterize anatomy and memory, based on deformation fields, and manganese accumulation. Voxel based analyses revealed significant differences in volume, T1 signal, and QSM. All image contrasts identified differences in the olfactory areas and hippocampus (Fig 2). Atrophy in olfactory areas, thalamus, and hippocampus was associated with increased magnetic susceptibility, and lower manganese uptake. The hypothalamus, entorhinal cortex, hippocampus and subiculum presented QSM decreases. QSM increases were noted in the caudate putamen and red nucleus.
For swim distances we detected a significant effect of time (F(4,88) = 22.44, p= 8.55e-13), and a trend for the genotype*time interaction (F(4,88) = 2.45, p= 0.05). Swim distances (mean±standard error of the mean) were longer in CVN-AD mice on days 3 (772.47±77.27 cm, versus 423.23±71.08 cm, t=3.33, df=22, CI=[131.5, 566.9] cm, p=0.003); and 4 (823.31±80.29 cm versus 458.96±73.86 cm; t=2.44, df=22, CI=[43.9,536.6] cm, p=0.003); and a trend on day 5 (578.49±88.04 cm, versus 330.98±80.99 cm, p=0.05).
To test whether specific regions predict behavioral decline we used sparse canonical correlation between brain regions and swim distance (at day 4). These regions included the fornix, hippocampus, and hypothalamus (Fig 3). The strength, and significance of the relationships between imaging markers and behavior increased when the model included all three factors (volume, T1w, QSM), except for the hypothalamus where the full and reduced models performed well (full model R2=0.69, p=0.03; T1w signal model R2=0.72, p=0.02). Our results support the hypothesis that combining multivariate predictors enhances the predictive ability for cognitive decline, relative to single biomarkers.