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To understand multifactorial conditions such as Alzheimers disease (AD) weneed brain signatures that predict the impact of multiple pathologies and theirinteractions. To help uncover the relationships between brain circuits andcognitive markers we have used mouse models that represent, at least in part,the complex interactions altered in AD. In particular, we aimed to understandthe relationship between vulnerable brain circuits and memory deficits measuredin the Morris water maze, and we tested several predictive modeling approaches.We used in vivo manganese enhanced MRI voxel based analyses to reveal regionaldifferences in volume (morphometry), signal intensity (activity), and magneticsusceptibility (iron deposition, demyelination). These regions included thehippocampus, olfactory areas, entorhinal cortex and cerebellum. The image basedproperties of these regions were used to predict spatial memory. We next usedeigenanatomy, which reduces dimensionality to produce sets of regions thatexplain the variance in the data. For each imaging marker, eigenanatomyrevealed networks underpinning a range of cognitive functions including memory,motor function, and associative learning. Finally, the integration ofmultivariate markers in a supervised sparse canonical correlation approachoutperformed single predictor models and had significant correlates to spatialmemory. Among a priori selected regions, the fornix also provided goodpredictors, raising the possibility of investigating how disease propagationwithin brain networks leads to cognitive deterioration. Our results supportthat modeling approaches integrating multivariate imaging markers providesensitive predictors of AD-like behaviors. Such strategies for mapping braincircuits responsible for behaviors may help in the future predict diseaseprogression, or response to interventions.

Multivariate MR Biomarkers Better Predict Cognitive Dysfunction in Mouse Models of Alzheimers Disease