Interpretable machine learning to synergize brain age estimation and neuroimaging genetics

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PROJECT SUMMARY Brain age can be used a predictor of deviation from typical age trajectories due to disease processes. Because brain age is strongly associated with neurodegenerative disease, brain age predicted from magnetic resonance images (MRIs) can become an affordable and noninvasive preclinical indicator of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) risk. Today’s best brain age estimation approaches use black-box machine learning (ML) that often lacks interpretability in the sense that it does not specify which neuroanatomic features are critical for brain age estimation. The first aim of this project is to design, test, and validate an interpretable ML architecture that leverages brain MRIs to estimate brain with high accuracy. We will construct an interpretable ML architecture trained on structural MRIs to identify neuroanatomic features that reflect brain age at the level of subjects and cohorts. These techniques will be tested and validated to ensure trustworthiness and generali- zability to new datasets. We hypothesize that our ML can use MRIs to predict MRI-derived brain age significantly more accurately than existing methods. Our second aim is to map neuroanatomic features that predict brain age and that reflect abnormal aging observed in MCI/AD. We will test the hypothesis that, aside from aging-related neuroanatomic features shared by cognitively normal subjects and MCI/AD patients, the latter exhibit additional neuroanatomic features that can distinguish them from the former, early during adulthood, with high sensitivity, specificity, and precision. Our third aim is to use genome-wide association (GWAS) to find genes associated with neuroanatomic features of brain aging that predict MRI-derived brain age. We will synergize our interpreta- ble ML approaches with GWAS to find genetic factors that affect brain aging features predictive of MCI/AD diag- nosis. We will develop and validate a polygenic risk score (PRS) of resilience/vulnerability to accelerated brain aging observed in MCI/AD. If successful, this project will deliver trustworthy, generalizable, and interpretable ML approaches that can leverage MRIs to identify novel brain aging features reflecting MCI/AD risk. Because aging is a lifelong process, we have the potential to detect such features much earlier than currently possible. Im- portantly, we will identify genes that act on brain aging in ways that may lead to MCI/AD. This can provide considerable insight on the potential mechanisms relating genetic factors to brain aging and MCI/AD.