Introduction

Aging is a fundamental process of life. Accelerated aging can cause a variety of problems, for example, the Alzheimer’s disease. It is well known that the grey matter volume of the brain reduces with aging. Using structural magnetic resonance imaging (sMRI) technique, we can non-invasively measure the grey matter volume of the human brain. This technology provides a unique opportunity to monitoring the aging process to see if it follows the standard trajectory, and altering when it deviates from the standard. It is important to build a model for age based on the grey matter volume, that depicts the normal trajectory of aging. One can use linear or nonlinear regression models to predict age by the grey matter volume, but they provide limited accuracy of the prediction.

Nowadays, the brain-inspired intelligent algorithms, such as deep learning, have been applied to many areas and made breakthrough in many applications. Here, we make available more than a thousand brain images to set up this challenge for the age prediction by the grey matter volume, hoping participants from multidiscipline could bring their intelligence together to build a model to precisely predict age from the brain image.

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