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

Publication date: Available online 19 October 2019Source: NeuroImage: ClinicalAuthor(s): Binke Yuan, Nan Zhang, Jing Yan, Jingliang Cheng, Junfeng Lu, Jinsong WuAbstractLanguage deficits following brain tumors should consider the dynamic interactions between different tumor growth kinetics and functional network reorganization. We measured the resting-state functional connectivity of 126 patients with left cerebral gliomas involving language network areas, including 77 patients with low-grade glioma (LGG) and 49 patients with high-grade glioma (HGG). Functional network mapping for language was performed by construction of a multivariate machine learning-based prediction model of individual aphasia quotient (AQ), a summary score that indicates overall severity of language impairment. We found that the AQ scores for HGG patients were significantly lower than those of LGG patients. The prediction accuracy of HGG patients (R2 = 0.27, permutation P = 0.007) was much higher than that of LGG patients (R2 = 0.09, permutation P = 0.032). The rsFC regions predictive of LGG's AQ involved the bilateral frontal, temporal, and parietal lobes, subcortical regions, and bilateral cerebro-cerebellar connections, mainly in regions belonging to the canonical language networks. The functional network of language processing for HGG patients showed strong dependence on connections of the left cerebro-cerebellar connections, limbic system, and the temporal, occipital, and prefrontal ...
Source: NeuroImage: Clinical - Category: Radiology Source Type: research