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| Home > Publications database > Structural connectome-based predictive modeling of cognitive deficits in treated glioma patients |
| Journal Article | FZJ-2023-04494 |
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2024
Oxford University Press
Oxford
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Please use a persistent id in citations: doi:10.1093/noajnl/vdad151 doi:10.34734/FZJ-2023-04494
Abstract: AbstractBackground. In glioma patients, tumor growth and subsequent treatments are associated with various types ofbrain lesions. We hypothesized that cognitive functioning in these patients critically depends on the maintainedstructural connectivity of multiple brain networks.Methods. The study included 121 glioma patients (median age, 52 years; median Eastern Cooperative OncologyGroup performance score 1; CNS-WHO Grade 3 or 4) after multimodal therapy. Cognitive performance was assessedby 10 tests in 5 cognitive domains at a median of 14 months after treatment initiation. Hybrid aminoacid PET/MRI using the tracer O-(2-[18F]fluoroethyl)-L-tyrosine, a network-based cortical parcellation, and advancedtractography were used to generate whole-brain fiber count-weighted connectivity matrices. The matrices were appliedto a cross-validated machine-learning model to identify predictive fiber connections (edges), critical corticalregions (nodes), and the networks underlying cognitive performance.Results. Compared to healthy controls (n = 121), patients’ cognitive scores were significantly lower in 9 cognitivetests. The models predicted the scores of 7/10 tests (median correlation coefficient, 0.47; range, 0.39–0.57) from0.6% to 5.4% of the matrix entries; 84% of the predictive edges were between nodes of different networks. Criticallyinvolved cortical regions (≥10 adjacent edges) included predominantly left-sided nodes of the visual, somatomotor,dorsal/ventral attention, and default mode networks. Highly critical nodes (≥15 edges) included the default modenetwork’s left temporal and bilateral posterior cingulate cortex.Conclusions. These results suggest that the cognitive performance of pretreated glioma patients is strongly relatedto structural connectivity between multiple brain networks and depends on the integrity of known networkhubs also involved in other neurological disorders.
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