## 1 Introduction
### Clinical Use Case  
Functional MRI (fMRI) is a method that eases the understanding of brain activation by analyzing the blood-oxygen-level-dependent (BOLD) signal, allowing the identification and localization of functional brain areas. The development of this technique promotes a better understanding of the functional anatomy of the human brain and a more accurate characterization of the inter-individual topographical variability in functional brain areas, such as language areas [13]. Thus, some fMRI techniques are progressively included as a procedure in several pathologies for surgical planning [14; 15; 16; 17]. The standard fMRI approach is a task-based block paradigm contrasting brain activation at rest and when performing a specific task. However, despite its usefulness, this technique presents several drawbacks, and inconsistencies [18]: the patient's cooperation is needed, and it is unsuitable for young children and patients unable to perform the task. In addition, the study of several functional networks is time-consuming, and it requires the acquisition of each network with subsequent development of a specific activation task paradigm [19]. An alternative for the task-based characterization of functional networks is the resting-state fMRI (rs-fMRI), which studies the synchronization of low-frequency oscillation between brain areas at rest [20; 21]. It is possible and practical to identify from these signals the so-called Intrinsic Connectivity Networks (ICNs), which reflect the neuro-anatomical substrate that corresponds to the brain's functional networks [22; 23]. However, rs-fMRI for functional network identification is not yet part of the pre-operative routine because of the high level of expertise needed for ICN identification. Indeed, each of the ICNs needs to be visually reviewed by an expert to identify an individual functional network of interest [24]. To broaden the use of this technique in the pre-surgical planning for various surgical procedures, the initial stage consists of the effective automation of fMRI brain network identification in patients' data. In the literature, automated machine learning algorithms have been the subject of several studies to identify disease patterns in rs-fMRI data, especially in epilepsy [25; 26],as well as traumatic brain injuries [27], addiction [28], cognitive impairment [29], and psychiatric disorders such as depression and schizophrenia [26; 28]. There have been relatively few attempts [22; 24; 30] to automatically identify functional networks on rs-fMRI data using machine learning. [PERSON] et al. [30] developed an instance-based automated method for identifying language networks in brain tumor subjects using independent component analysis (ICA)-based mapping on rs-fMRI. By contrast, we are data-driven and do not limit ourselves to only language networks. In fact, our study considers seven functional networks. Each of these studies has its defined scopes, data variants, and functional networks used for automated identification in rs-fMRI for pre-surgical planning. In [24], the authors proposed a task-free paradigm for acquiring fMRI data, which was less demanding for patients and easy to administer. Further investigation was carried out on right-handed healthy control subjects. A semi-automated language component identification procedure was proposed and tested on healthy patients [24]. In this article, we consider unhealthy patients in addition to healthy subject data. In the study by [22], a model was trained to identify the main functional networks in a small number of healthy volunteers for different functional networks. The performance of the simple feed-forward network proposed in [22] is ultimately dependent on handcrafted features extracted from fMRI images. The above concerns motivated our proposition to design a specific end-to-end deep learning [31; 32] knowledge transfer method to identify and automate the detection of functional networks in the rs-fMRI of unhealthy patients.