Resting-state functional connectivity predicts individual language impairment of patients with left hemispheric gliomas involving language network.

•Multivariate machine learning-based prediction models of rsFCs were constructed to predict individual aphasia quotient (AQ) for LGG and HGG patients.•Individual whole brain rsFCs significantly predicated AQ for LGG and HGG patients.•Canonical language regions and their connections showed most predictive contributions for the LGG model, suggesting large functional reshaping.•The functional network of HGG patients for language processing showed heavy dependence on connections of the left cerebro-cerebellar connections, limbic system, and the temporal, occipital, and prefrontal lobes.