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Emerging evidence showed that major depressive disorder (MDD) is associated with disruptions of brain structural and functional networks, rather than impairment of isolated brain region. Thus, connectome-based models capable of predicting the depression severity at the individual level can be clinically useful. Here, we applied a machine-learning approach to predict the severity of depression using resting-state networks derived from source-reconstructed Electroencephalography (EEG) signals. Using regression models and three independent EEG datasets (N=328), we tested whether resting state functional connectivity could predict individual depression score. On the first dataset, results showed that individuals scores could be reasonably predicted (r=0.61, p=4 x 10-18) using intrinsic functional connectivity in the EEG alpha band (8-13 Hz). In particular, the brain regions which contributed the most to the predictive network belong to the default mode network. We further tested the predictive potential of the established model by conducting two external validations on (N1=53, N2=154). Results showed high significant correlations between the predicted and the measured depression scale scores (r1= 0.49, r2=0.37, p<0.001). These findings lay the foundation for developing a generalizable and scientifically interpretable EEG network-based markers that can ultimately support clinicians in a biologically-based characterization of MDD.

Most brain disorders including Alzheimer's disease (AD) are related to alterations in the normal brain network organization and function. Exploring these network alterations using non-invasive and easy to use technique is a topic of great interest. In this paper, we collected EEG resting-state data from AD patients and healthy control subjects. Functional connectivity between scalp EEG signals was quantified using the phase locking value (PLV) for 6 frequency bands. To assess the differences in network properties, graph-theoretical analysis was performed. AD patients showed decrease of mean connectivity, average clustering and global efficiency in the lower alpha band. Positive correlation between the cognitive score and the extracted graph measures was obtained, suggesting that EEG could be a promising technique to derive new biomarkers of AD diagnosis.

Epilepsy is a network disease. The epileptic network usually involves spatially distributed brain regions. In this context, noninvasive M/EEG source connectivity is an emerging technique to identify functional brain networks at cortical level from noninvasive recordings. In this paper, we analyze the effect of the two key factors involved in EEG source connectivity processing: i) the algorithm used in the solution of the EEG inverse problem and ii) the method used in the estimation of the functional connectivity. We evaluate four inverse solutions algorithms and four connectivity measures on data simulated from a combined biophysical/physiological model to generate realistic interictal epileptic spikes reflected in scalp EEG. We use a new network-based similarity index (SI) to compare between the network identified by each of the inverse/connectivity combination and the original network generated in the model. The method will be also applied on real data recorded from one epileptic patient who underwent a full presurgical evaluation for drug-resistant focal epilepsy. In simulated data, results revealed that the selection of the inverse/connectivity combination has a significant impact on the identified networks. Results suggested that nonlinear methods for measuring the connectivity are more efficient than the linear one. The wMNE inverse solution showed higher performance than dSPM, cMEM and sLORETA. In real data, the combination (wMNE/PLV) led to a very good matching between the interictal epileptic network identified from noninvasive EEG recordings and the network obtained from connectivity analysis of intracerebral EEG recordings. These results suggest that source connectivity method, when appropriately configured, is able to extract highly relevant diagnostic information about networks involved in interictal epileptic spikes from non-invasive dense-EEG data., Comment: 30 pages, 5 figures

Exploring the human brain networks during rest is a topic of great interest. Several structural and functional studies have previously been conducted to study the intrinsic brain networks. In this paper, we focus on investigating the human brain network topology using dense Electroencephalography (EEG) source connectivity approach. We applied graph theoretical methods on functional networks reconstructed from resting state data acquired using EEG in 14 healthy subjects. Our findings confirmed the existence of sets of brain regions considered as functional hubs. In particular, the isthmus cingulate and the orbitofrontal regions reveal high levels of integration. Results also emphasize on the critical role of the default mode network (DMN) in enabling an efficient communication between brain regions., Comment: International Conference on Bio-engineering for Smart Technologies (BioSMART 2016)