Topology matters: characteristics of functional brain networks in healthy subjects and patients with Epilepsy, Diabetes, or Amyotrophic Lateral Sclerosis during a resting-state paradigm.

The brain can be seen as a complex structural and functional network. Cognitive functioning strongly depends on the organization of functional brain networks. EEG/MEG resting-­‐state functional connectivity and functional brain networks studies attempt to characterize normal brain organization as well as deviation from it due to brain diseases. Despite the impact on the understanding of brain functioning that these tools provided, there are still methodological hurdles that might compromise the quality of the results. The main aim of this thesis was to gain an understanding of the role of functional connectivity and network topology on brain functioning by: (i) addressing the methodological issues intrinsic in the analysis that can bias the results; (ii) quantifying functional connectivity differences possibly induced by brain impairments; (iii) detecting and quantifying how network topology changes, due to brain impairments. In order to achieve these objectives, functional connectivity and functional brain networks obtained by empirical recordings were reconstructed. Recordings were acquired with different modalities (EEG or MEG) and under different pathologies: epilepsy, diabetes and amyotrophic lateral sclerosis. Specifically three research questions were addressed: • Do functional brain network architectures obtained from pharmaco-­‐resistant epileptic patients responding to vagal nerve stimulation (VNS) change compared to patients not responding to VNS? • Are functional connectivity alterations related to cognitive performance and clinical status in type I diabetes mellitus patients? • Is functional network topology related to disease duration in amyotrophic lateral sclerosis patients? In order to answer these questions, avoiding possible biases which may affect the results, two key choices were made: first, the selection of the phase lag index [1] as functional connectivity estimator because it is less sensible to common sources problem; second, the application of minimum spanning tree (MST) [2] approach to overcome the problem of network comparison and characterize network topology reliably. In summary, this thesis confirms that alterations of functional connectivity and functional brain networks in disease may be used as potential biomarkers for more objective diagnosis and the choice of effective treatment options. Specifically, in epileptic patients implanted with VNS the relation between network measures and clinical benefit suggest that these measures can be used as a marker in monitoring the efficacy of the treatment; in amyotrophic lateral sclerosis the relation between disease duration and whole brain network disruption suggests diagnostic relevance of network measures in evaluating and monitoring the disease; and finally in type 1 diabetic mellitus patients functional connectivity measures can be complementary to cognitive tests and may help to monitor the effect of T1DM on brain functions.