Intratumoral and Peritumoral Functional Connectivity Reveals Network-Level Signatures of Glioma.

Authors

Hongbo Bao, Yan Bai, Jie Hu, Wencai Ding, Jiacheng Lai, Andrew Zalesky, Ye Ella Tian, Yanmei Tie, Janet Mei, Shengyu Fang, Xing Fan, Licia P Luna, Craig K Jones, Jianxun Ren, Weigang Cui, Jiadong Yan, Zeya Yan, Zihan Wang, Renwu Zhang, Zhonghua Lv, Yihang Jiao, Tao Jiang, Peng Liang, Haris I Sair, Yinyan Wang

Published in

MedComm. Volume 7. Issue 11. Pages e71083. Epub Oct 17, 2026.

Abstract

Gliomas are recognized as network-level diseases that remodel functional connectivity across the brain. Using resting-state functional MRI (rs-fMRI) data from 306 patients with pathologically confirmed glioma at two hospitals, we delineated individualized intra- and peritumoral functional networks to quantify tumor-related alterations. Two novel indices-the ratio of intratumoral functional regions (RIFR) and ratio of peritumoral tumor-related regions (RPTR)-were proposed to describe network remodeling. Both indices were related to pathological, structural, functional, cognitive, language, and survival measures. Lower RIFR indicated diminished intratumoral connectivity and worse prognosis, while higher values reflected preserved network integration and longer survival. Models incorporating network features yielded areas under the receiver operating characteristic curve of 0.90 in training and 0.86-0.88 in external validation. Supporting analyses suggested that intratumoral functional regions are associated with distinct molecular and cellular characteristics. These findings suggest that intra- and peritumoral networks reflect biologically and functionally distinct tumor subregions and are associated with network-level alterations, brain function, and patient outcome. Interpretation is limited by the hemodynamic basis of rs-fMRI, incomplete data, and limited intraoperative validation. These findings identify individualized intra- and peritumoral connectivity measures as candidate markers of glioma-associated network remodeling and prognosis that require prospective, multimodal validation.

PMID:
42892999
Bibliographic data and abstract were imported from PubMed on 18 Oct 2026.

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