## 1 Introduction  
Brain tumors are disruptive lesions that lead to functional modifications of brain networks, known as functional reorganization or plasticity [1]. Short- and long-range effects have been shown using task-based and resting-state fMRI in patients with gliomas [2], possibly representing an adaptive phenomenon to compensate for tumor-induced clinical deficits. A better understanding of functional reorganization could optimize neurosurgical resection of brain tumors and tailor targeted therapies that enhance recovery [3]. Conversely, functional modifications induced by focal lesions may fail to compensate for lost functions or may even be detrimental. For example, right-hemispheric activation in left-sided stroke patients has been associated with failure of perilesional reorganization and a worse outcome [4]. When a focal lesion invades eloquent brain areas, the damage can trigger disinhibition of nearby neural networks, creating a permissive environment that may lead to compensatory (adaptive) or detrimental (maladaptive) plasticity [5]. Knowledge of plasticity dynamics and causative factors is lacking in the literature, despite being necessary for the highlighting of candidates for reorganization and also for the distinguishing of adaptive from maladaptive plasticity, which is a prerequisite for clinical implementation. Tumor pathology and location likely influence the network dynamics and may help to distinguish different types of plasticity. Classically, plastic changes have been described in low-grade gliomas (LGG) with a progressive timeline [6] from intra-tumoral and perilesional reorganization to activation of contralateral homologues. Some evidence supports the compensatory nature of plastic changes in LGG [7]. On the other hand, reorganization in high-grade gliomas (HGG) has not yet been demonstrated to be clinically meaningful. The location of a lesion may directly influence the magnitude of functional changes. For example, damage to brain regions important for communication between subnetworks (connectors) causes greater effects than damage to peripheral areas [8]. According to this model, damage to specific brain areas should lead to greater network changes. Graph theory is applied to fMRI to study the architecture and complexity of functional networks [9; 10] with relevant clinical applications [11]. Newly diagnosed gliomas demonstrated globally altered functional connectomic profiles in previous studies [12], while distributed changes of the functional connectome have been shown in glioblastoma patients after surgery [13]. Graph theory metrics can describe how efficient, integrated and connected a network is, quantifying properties such as small-worldness, expression of efficient information segregation and integration at low wiring and energy costs [14] (Supplementary Table S1). Starting from this background, we studied the network modifications of left-hemispheric LGG and HGG versus healthy controls (HC) by applying graph theory to resting-state fMRI, with the following objectives: (1) compare the network modifications induced by tumors of different grades with respect to HC; (2) compare left vs. right-hemispheric network changes; (3) compare the network modifications induced by tumors of different locations with respect to HC. We selected only left-hemispheric tumors to be able to study ipsilateral and contralateral tumor effects in a uniform population. We investigated patients' language performance as an example of left-lateralized function through the Boston Naming Test and neurologic/neurosurgical assessment of aphasia to identify possible clinical correlates of network changes. We hypothesized that tumors of different grades and locations would demonstrate characteristic intra-hemispheric and interhemispheric functional connectivity changes. Particularly, we hypothesized that HGG would display predominant regional modifications due to neurovascular uncoupling, while LGG would demonstrate both ipsi- and contralateral changes suggestive of reorganization.