Indirect functional connectivity does not predict overall survival in glioblastoma

• Published in: Neurobiology of disease Vol. 196; p. 106521
• Main Authors: Pini, Lorenzo, Lombardi, Giuseppe, Sansone, Giulio, Gaiola, Matteo, Padovan, Marta, Volpin, Francesco, Denaro, Luca, Corbetta, Maurizio, Salvalaggio, Alessandro
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.06.2024; Elsevier
• Subjects: Adult; Aged; Brain - diagnostic imaging; Brain - physiopathology; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - mortality; Brain Neoplasms - physiopathology; Connectome - methods; Disconnections; Female; Functional imaging; Glioblastoma; Glioblastoma - diagnostic imaging; Glioblastoma - mortality; Glioblastoma - physiopathology; Humans; Magnetic Resonance Imaging - methods; Male; Middle Aged; Nerve Net - diagnostic imaging; Nerve Net - physiopathology; Retrospective Studies; Survival prediction; Disconnections; Survival prediction; Glioblastoma; Functional imaging
• ISSN: 0969-9961; 1095-953X; 1095-953X
• DOI: 10.1016/j.nbd.2024.106521

Lesion network mapping (LNM) is a popular framework to assess clinical syndromes following brain injury. The classical approach involves embedding lesions from patients into a normative functional connectome and using the corresponding functional maps as proxies for disconnections. However, previous studies indicated limited predictive power of this approach in behavioral deficits. We hypothesized similarly low predictiveness for overall survival (OS) in glioblastoma (GBM). A retrospective dataset of patients with GBM was included (n = 99). Lesion masks were registered in the normative space to compute disconnectivity maps. The brain functional normative connectome consisted in data from 173 healthy subjects obtained from the Human Connectome Project. A modified version of the LNM was then applied to core regions of GBM masks. Linear regression, classification, and principal component (PCA) analyses were conducted to explore the relationship between disconnectivity and OS. OS was considered both as continuous and categorical (low, intermediate, and high survival) variable. The results revealed no significant associations between OS and network disconnection strength when analyzed at both voxel-wise and classification levels. Moreover, patients stratified into different OS groups did not exhibit significant differences in network connectivity patterns. The spatial similarity among the first PCA of network maps for each OS group suggested a lack of distinctive network patterns associated with survival duration. Compared with indirect structural measures, functional indirect mapping does not provide significant predictive power for OS in patients with GBM. These findings are consistent with previous research that demonstrated the limitations of indirect functional measures in predicting clinical outcomes, underscoring the need for more comprehensive methodologies and a deeper understanding of the factors influencing clinical outcomes in this challenging disease. [Display omitted] •This study challenges the predictive power of network mapping in glioblastoma.•We found a lack of distinctive functional network patterns associated with survival.•The clinical value of indirect brain functional tools needs further investigation.