Disrupted dynamics of transient brain states in patients with brain tumors: a co-activation pattern analysis of...

Brain tumors impair brain function both locally and across distant regions by disrupting network connectivity, contributing to cognitive deficits and triggering compensatory plasticity. Traditional resting-state fMRI methods average brain activity over time, missing transient, dynamic events critical to cognition. Co-Activation Pattern (CAP) analysis captures these brief brain states, enabling quantification of state engagement and duration.

To investigate alterations in transient brain states in patients with brain tumors using CAP analysis of resting-state fMRI, and to assess whether these changes reflect modified engagement of cognitive states compared to healthy controls.

This retrospective cross-sectional study included 106 patients with left-hemispheric brain tumors (72 high-grade gliomas, 19 low-grade gliomas, 15 metastases; mean age 61.15 ± 8.95 years; 43 women) and 100 age-matched healthy controls. Resting-state fMRI data were analyzed using a seed-free clustering method (TbCAPs toolbox) to extract CAPs. CAPs were first identified in controls and then matched to patients via spatial similarity. Each CAP was assigned to a canonical brain network using the GIFT toolbox. Dynamic metrics computed included: persistence (duration of a CAP), transitions (switching frequency), in-degree, and out-degree. Group comparisons used two-tailed t-tests with Benjamini-Hochberg correction (p < 0.05).

Six CAPs were identified. Compared to controls, patients showed significantly increased transitions, in-degree, and out-degree, and decreased persistence in CAPs linked to the default mode and executive control networks (all p < 0.01), suggesting more frequent but less stable engagement. Visuospatial network CAPs demonstrated lower transition, in/out-degree and persistence in patients (p < 0.05). No significant differences were observed among tumor types.

Patients with brain tumors display altered CAP dynamics involving higher-order cognitive networks and perceptual networks. These alterations may reflect the combined effects of tumor-related network damage and potential adaptive reorganization, with potential implications for functional preoperative planning and prognosis. However, in the absence of direct clinical or neuropsychological correlations, these interpretations remain hypothesis-generating. The findings are also limited by the retrospective cross-sectional design preventing causal or longitudinal interpretation, and the restriction to left-hemispheric tumors. Future studies integrating CAP dynamics with cognitive and clinical outcomes will be necessary to determine whether these changes reflect compensatory functional reorganization in brain tumor patients.