Constructing Reliable Network Of Biomarker Covariance By Joint Data Harmonization And Graph Learning

Abstract

Networks of biomarker covariance based on neuropathological events or neuro-degeneration degree is important to understand genetic influence and trophic reinforcement in the brain development/aging process. It is a common to quantiry the covariance of inter-subject biomarker profiles by linear correlation metrics such as Pearson’s correlation. Due to the heterogeneity and noise in the observed neurobiological data, however, it is difficult to construct a reliable covariance network using gross statistical measurement. To this, we propose a graph learning approach to infer the brain connectivity based on the harmonized inter-subject biomarker profiles. Specifically, we progressively estimate brain network until region-to-region connectivities reach the largest consensus of biomarker covariance across individuals. A better understanding of the network topology allows us to harmonize the neurobiological data effectively which eventually facilitates the graph inference. Since the network of biomarker covariance represents the region-wise associations in the entire population, we further promote diversity by adaptively penalizing the predominant influence from a group of biomarker profiles exhibiting statistically correlated patterns. We applied our method to the cortical thickness from MRI and amyloid-beta burden from PET images, which are biomarkers in Alzheimer’s disease (AD). Enhanced statistical power and replicability have been achieved by our approach in identifying network alterations between cognitive normal (CN) and AD cohorts.