Brain Networks as a Predictor of Aging Along Lifespan

The chronological age (CHA) differs from the biological one. While the former is defined as the time running as birth, the latter quantifies the maturity level that an individual (or an organ) has at the operational level. In relation with the brain, the discrepancy between the brain age and CHA might work as a biomarker for quantifying deterioration as a result of disease or improvement after some treatment or therapy, which has unlimited applications. Here, I will talk about recent results from our laboratory addressing whether or not  the CHA of an individual can be predicted  from the brain connectivity patterns. In particular, by building functional (resting-state) and structural (tractography) brain networks from neuroimages of healthy participants (N=155) ranging from 10 to 80 years, we apply machine learning methods to define  the brain-connectome age (BCA) –the age estimator resulting from the structure-function connectome–. We next compared BCA to CHA to find that   the connectivity between basal ganglia and thalamus to frontal areas, a.k.a. the fronto-striato-thalamic circuit, makes the major contribution to age estimation. Notably, the same methodology can be generally applied to identify the structural-functional brain network patterns correlating to other biomarkers than CHA.

Reference:
http://jesuscortes.info/jesusweb/publications/Betal_HBM2018.pdf