We used multivariate analytical algorithms and network approaches to extract large-scale brain network information of individual subjects from multi-modality neuroimage database. These network-based indices with machine learning / artificial intelligence approaches could be to produce a single quantitative index for each individual. The index will be provided an objective way to serve as a potential image biomarker for clinical disorders and aging process.

1). The unique neuroimaging database is based on the demographic properties in Taiwan and the findings and prediction models can be generalized for Asia populations. 2). we obtain more subtle aging information from multi-modality neuroimage dataset with optimized imaging acquisition schemes. 3). We provide a framework to predict individual brain health in aging trajectory by combining feature of the brain.

We aim to provide a simple objective quantitative index to predict individual brain health from multi-modality neuroimage dataset. This biomarker can be further applied in public health domains and clinical applications. Moreover, the proposed framework also can be used in fields of personal risk evaluation, healthcare intervention, preventive medicine and insurance field.