The Vital Role of Central Executive Network in Brain Age: Evidence From Machine Learning and Transcriptional Signatures - Fang, Keke; Han, Shaoqiang; Li, Yuming; Ding, Jing; Wu, Jilian; Zhang, Wenzhou (Frontiers in Neuroscience, 2021)
Recent studies combining neuroimage with machine learning method successfully infer individual’s brain age and its discrepancy with the chronological age is used to identify age-related diseases. However, which brain networks play decisive roles in brain age prediction and the underlying biological basis of brain age remain unknown. To answer these questions, we estimated individual’s brain age in Southwest University Adult Lifespan Dataset (N = 492) from gray matter volumes (GMV) derived from T1-weighted MRI scans by means of gaussian process regression. The computational lesion analysis was performed to determine the importance of each brain network in brain age prediction. Then, we identified brain age related genes by using prior brain-wide gene expression data, followed by gene enrichment analysis using Metascape. As a result, the prediction model successfully inferred individual’s brain age and computational lesion prediction results identified central-executive network as a vital network in brain age prediction (Steiger’s Z = 2.114, p = 0.035). What’s more, the brain age related genes were enriched for GO processes/KEGG pathways grouped into numbers of clusters such as regulation of iron transmembrane transport, synaptic signaling, synapse organization, retrograde endocannabinoid signaling (e.g. dopaminergic synapse), behavior (e.g. memory, associative learning), neurotransmitter secretion and dendrite development. In all, these results revealed that GMV of central-executive network played a vital role in predicting brain age and bridged the gap between transcriptome and neuroimaging promoting an integrative understanding pathophysiology of brain age.