Prediction of brain age based on the community structure of functional networks

Studies have demonstrated that brain functional networks (BFNs) and machine learning are promising methods for predicting brain age. However, many studies ignore the important structural properties of BFNs and the feature alterations of community structure in healthy aging brains. Given this issue, we developed a novel scheme to predict brain age based on resting-state functional magnetic resonance imaging (rs-fMRI). Firstly, motivated by the fact that the brain is organized with sparsity, modular and other properties, we established a novel BFNs model using the nonnegative block diagonal representation (NBDR) with block diagonal matrix-induced regularization. Secondly, the collective sparse symmetric nonnegative matrix factorization (cssNMF) was adopted to detect the individual-level community strengths as a mesoscale network topology structure, which combined with small-scale and large-scale network topologies, were used as features. Finally, brain age was predicted by using six different models with a machine learning framework based on the extracted features. Experimental results on both simulated and real data revealed that the novel BFNs method was superior to other compared methods in connectivity, and the feature extraction scheme proposed performed well in predicting brain age. This research provides a novel insight into the construction of BFNs and the feature extraction of brain signals, which is useful to understand the mechanism of normal brain aging.