Table 2.

Functional connectivity profile showing significant effects revealed by a linear mixed model of longitudinal changes (baseline, 1–3-, 3–6-, 6–9-, 9–15-month follow-ups) in brain tumor patients stratified into two groups (low-grade gliomas (LGG) correspond to gliomas treated with only surgical resection, while high-grade gliomas (HGG) correspond to gliomas treated with surgical resection in combination with radiotherapy and/or chemotherapy). The model included the following main predictors: time (a positive effect means longitudinal connectivity increases regardless of tumor grade and lateralization), time ×tumor grade interaction (a positive effect means that longitudinal connectivity increases faster in LGG relative to HGG, regardless of lateralization) and time ×tumor lateralization (positive means that longitudinal connectivity in left-lateralized glioma increases faster than in right-lateralized glioma, regardless of tumor grade). The model was adjusted with age, sex, baseline tumor volume, IDH mutation, tumor methylation, and tumor WHO grade as nuisance variables. Significant (p < 0.05) fixed effects are emphasized in bold with their respective effect sizes and an asterisk if the effect survives FDR correction across different models

Response	Predictors [Estimate β, p-value, Cohen’s d if significant]			Random effects
	Time	Time × tumor grade	Time × tumor lateralization	σ2
Within default -mode network	[0.003, 0.54]	[0.00002, 0.01, 0.001]*	[0.006, 0.001, 0.28]*	0.01
Within fronto-parietal network	[0.006, 0.28]	[ – 0.01, 0.63]	[ – 0.003, 0.11]	0.01
Within hubs	[0.002, 0.68]	[ – 0.003,  – 0.15]	[0.005, 0.0004, 0.29]*	0.01
Within no-hubs	[ – 0.0006, 0.44]	[0.0010, 0.71]	[ – 0.001, 0.004,  – 0.30]*	0.0004
Between hubs-no-hubs	[0.0004, 0.75]	[ – 0.001, 0.65]	[0.0023, 0.005, 0.40]*	0.0001

*p-value_FDR < 0.05