Resting State Brain Connectivity analysis from EEG and FNIRS signals

Contemporary neuroscience is highly focused on the synergistic use of machine learning and network analysis. Indeed, network neuroscience analysis intensively capitalizes on clustering metrics and statistical tools. In this context, the integrated analysis of functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) provides complementary information about the electrical and hemodynamic activity of the brain. Evidence supports the mechanism of the neurovascular coupling mediates brain processing. However, it is not well understood how the specific patterns of neuronal activity are represented by these techniques. Here we have investigated the topological properties of functional networks of the resting-state brain between synchronous EEG and fNIRS connectomes, across frequency bands, using source space analysis, and through graph theoretical approaches. We observed that at global-level analysis small-world topology network features for both modalities. The edge-wise analysis pointed out increased inter-hemispheric connectivity for oxy-hemoglobin compared to EEG, with no differences across the frequency bands. Our results show that graph features extracted from fNIRS can reflect both shortand longrange organization of neural activity, and that is able to characterize the large-scale network in the resting state. Further development of integrated analyses of the two modalities is required to fully benefit from the added value of each modality. However, the present study highlights that multimodal source space analysis approaches can be adopted to study brain functioning in healthy resting states, thus serving as a foundation for future work during tasks and in pathology, with the possibility of obtaining novel comprehensive biomarkers for neurological diseases.