Visualization of the human brain using complex images such as tractography, functional connectivity, functional imaging, brain volume, PET, SPECT, etc. is a significant challenge, and the fusion of such types of images produce an even more complex problem. Also, the increased use of functional connectivity based on functional MRI (fMRI) techniques, and the visualization of the complex networks obtained by this method, creates the need for advanced tools to visualize the various functional connectivity networks for academic purposes and research.

Functional connectivity is defined as the time dependence of neuronal activity between anatomically separate brain regions (Proal et al., 2011). There are multiple functional connectivity networks in a healthy brain. Yeo et al. (2011) with his network-level parcellation determined that there are at least seven standard functional connectivity networks in the healthy human brain. These are visual network, somatomotor, dorsal attention, ventral attention, limbic, frontoparietal and default network (default mode network, DMN). Examples of this are the (consisting of precuneus, medial frontal, inferior parietal cortical regions and medial temporal lobe), which is active during wakeful rest and deactivates during most externally oriented tasks (Raichle et al., 2001; Greicius, 2008; de la Iglesia-Vayá et al., 2013); and the somatomotor network related to sensitive and primary motor processing (de la Iglesia-Vayá et al., 2013).

Different neuroimaging visualization techniques have been proposed. For example, Margulies et al. (2013) describe 2-D and 3-D methods to display anatomical information, tractography and functional connectivity. Rojas et al. (2014) describe a method for displaying stereoscopic 3-D neuroimages by using red-cyan colors. Rojas et al. (2014), show examples of tractography, brain volumetry, and quantification of multiple sclerosis lesions and images of functional connectivity (network visualization).

Another brain test is Electroencephalography (EEG), a well-known electrophysiological diagnosis method that measures the electrical activity of the brain. It is a noninvasive technique in which electrodes are attached over the scalp. There are two systems for the placement of electrodes: 10–20 corresponding to 21 electrodes (Jasper, 1958; Klem et al., 1999), and 10–10 which corresponds to 65 electrodes (Nuwer et al., 1998). EEG is the principal diagnostic tool for diseases such as epilepsy.

Mobile devices (such as smartphones and tablets) are becoming increasingly common among users. In a study published by Scott Wilson on November 6, 2013 (Wilson, 2013), sales of phones and tablets are increasing in an annual growth rate of 21.8% (Table 1), implying a broad base of potential users for each application. Mainly, there are four operating systems for mobile devices: iOS used by Apple devices (13.9% of iPhone and iPad), Android used by Samsung Electronics Co Ltd., LG Electronics Inc., Sony Corporation, Google Inc., HTC Corporation, Motorola Inc., Huawei, Xiaomi, etc. (corresponding to 82.8%), Windows Phone (2.6%), Blackberry OS (0.3%) and others (0.4%; IDC Research, Inc., 2015). In the medical field, 79.0% of medical students and 74.8% of junior doctors in United Kingdom owned a smartphone (Payne et al., 2012), and in USA 87% of medical doctors use a smartphone or tablet device in their workplace (Ventola, 2014).

In this article, we describe two applications for iOS and Android-based mobile devices covering 96.7% of the mobile market. One application shows the seven standard functional connectivity networks (Yeo et al., 2011) superimposed on a cerebral cortex (transparent brain) and the other one interactive application shows 21 10–20 EEG electrodes over the Yeo et al. (2011) standard networks.

Two applications called iBraiN2 and iBraiNEEG2 were developed for the visualization of Functional connectivity networks.

Both applications show the advanced uses that iOS and Android-based mobile devices (tablets and smartphones) can be given in the medical field. As far as we know, iBraiN2 and iBraiNEEG2 are the first functional connectivity applications for iOS/Android-based mobile devices. These applications are different from previous work: (i) the atlas in Rojas et al. (2014), which is a functional connectivity anaglyph, based in area-level parcellation (Craddock et al., 2012) for visualization in PC with 3D Slicer software; and (ii) iBraiN2 and iBraiNEEG2, which are mobile based applications with functional connectivity networks, based in network-level parcellation (Yeo et al., 2011).

iBraiN2 is a tool for academic use that shows the three-dimensional position of each functional connectivity network in the cerebral cortex. Using a transparent brain, it is possible to see the position of each region in its connectivity network or the relative position of each network. Although this 3D representation only shows a fixed dataset, is useful for academic purposes for neurologists, neurosurgeons, radiologists, medical students, neuroscientists and researchers in neuroimaging, among other professionals, since it allows them to easily understand in an interactive way the anatomy and distribution of the brain’s functional networks, because it shows the standard functional connectivity networks (Yeo et al., 2011).

iBraiNEEG2 shows the relative position of the 10–20 EEG electrodes with respect to the functional connectivity networks. This application helps the EEG analysis regarding standard functional connectivity networks (Yeo et al., 2011) and combines EEG data and functional connectivity obtained with fMRI to support the analysis of these techniques and research into diseases such as epilepsy. For example, with the EEG data of the epileptic focus of a patient, it could be possible to get the RS-fMRI functional connectivity network that is affected.

Should the scientific community adopt a brain atlas as a functional connectivity gold standard, future updates of the applications would greatly benefit from the use of such atlas. A very relevant candidate for this standard would be a recently published brain atlas (Glasser et al., 2016) that uses multi-modal magnetic resonance images from the Human Connectome Project (HCP).

In conclusion, the applications for mobile devices shown here are useful for education and training purposes (medical professionals) in functional connectivity related topics and EEG analysis regarding standard functional connectivity networks. Also, the advantage of applications for mobile devices is associated with their portability and availability.

GMR: article writing, creation of the figures, mobile applications design, mobile applications testing (Android), creation and optimization of functional connectivity networks mesh. JAF: mobile applications design and programming, mobile applications testing (Android). MG: mobile applications design, mobile applications testing (iOS), article review and correction.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.