## 2 Materials and Methods  
### Database  
We obtained data from 81 healthy subjects and 55 unhealthy patients. While healthy data were acquired from regular volunteers, unhealthy data were obtained from patients with brain tumors with a specific lesion region, as indicated by the provided binary lesion mask. A detailed description of the unhealthy population is provided in [13]. This is a single-center, prospective, open-label trial, in compliance with regulation and ethical guidelines for clinical research, approved by the local ethics committee (Comite de protection des personnes Ouest II, decision reference CPP 2012-25). A total of 81 healthy volunteers (36 females and 45 males) aged from 23 to 38 years old were included and signed written informed consent. Fifty-five adult patients with a brain lesion treated in the Department of Neurosurgery of the university hospital of Angers underwent a preoperative fMRI language mapping with both rs-fMRI and task fMRI, as well as a perioperative cortical mapping of eloquent brain language areas in awake condition. All subjects gave their written, informed consent before enrolling in this study.  
For all healthy and unhealthy data, we extracted 55 features ICA with a specific interest in 7 brain features. One of the main difficulties with independent component analysis in resting-state fMRI is the determination of the total number of components (TNC) to be used, which may lead to suboptimal decompositions with the merging of multiple networks in case of low TNC or the fragmentation of a functional network into multiple components in case of high TNC [37,38]. Our choice to analyze 55 ICs among all patients was based on previous works and appeared to be a good compromise to identify functional brain networks [23,39].  
These brain features correspond to seven biological networks of the brain, which are the Language Network (LANG), Salience Network (SAL), Ventral Attention Network (VAN), Default Mode Network (DMN), Left Fronto-parietal Control (IFPCN), Right Fronto-parietal Control Network (rFFCN), Dorsal Attention Network (DAN). The seven selected brain features represent the main ICN identified and described in resting-state fMRI literature. These particular networks were selected for the DMN to serve as a control for the others because of the inter-individual variability that makes them difficult to identify using detection software or by non-expert reviewers. These connectivity networks correspond to known functional networks that support cognitive functions and have been used for pre-surgical planning [38,40]. The connectivity networks were also found to be consistent between rs-fMRI and various fMRI data acquisition and analysis techniques [41]. Functional networks without anatomical variabilities, such as the motor, sensory, or visual cortex, were not considered for algorithm training and automated identification because of their consistent anatomical location.  
Image labels for each healthy and unhealthy data file marked by domain experts were used to assign each image to its respective network class. In addition to the two variants of network images provided for both healthy and unhealthy, unhealthy data include details of the brain tumor as described in Table 1 and shown in Figure 1.  
Figure 1: Visual representation of the described data of unhealthy patients in Table 1: (**a**) is the lesion mask, (**b**) is the grey matter mask, (**c**) is white matter mask, (**d**) is cerebrospinal fluid mask, (**e**) is whole brain, cerebrospinal fluid (skull and skin included), (**f**) is whole brain (white and grey matter).