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Alterations of brain network activity are observable in Alzheimer's disease (AD) together with the occurrence of mild cognitive impairment, before overt pathology. However, in humans as well in AD mouse models, identification of early biomarkers of network dysfunction is still at its beginning. We performed in vivo recordings of local field potential activity in the dentate gyrus of PS2APP mice expressing the human amyloid precursor protein (APP) Swedish mutation and the presenilin-2 (PS2) N141I. From a frequency-domain analysis, we uncovered network hyper-synchronicity as early as 3 months, when intracellular accumulation of amyloid beta was also observable. In addition, at 6 months of age, we identified network hyperactivity in the beta/gamma frequency bands, along with increased theta-beta and theta-gamma phase-amplitude cross-frequency coupling, in coincidence with the histopathological traits of the disease. Although hyperactivity and hypersynchronicity were respectively detected in mice expressing the PS2-N141I or the APP Swedish mutant alone, the increase in cross-frequency coupling specifically characterized the 6-month-old PS2APP mice, just before the surge of the cognitive decline.

Brain networks are complex dynamical systems in which directed interactions between different areas evolve at the sub-second scale of sensory, cognitive and motor processes. Due to the highly non-stationary nature of neural signals and their unknown noise components, however, modeling dynamic brain networks has remained one of the major challenges in contemporary neuroscience. Here, we present a new algorithm based on an innovative formulation of the Kalman filter that is optimized for tracking rapidly evolving patterns of directed functional connectivity under unknown noise conditions. The Self-Tuning Optimized Kalman filter (STOK) is a novel adaptive filter that embeds a self-tuning memory decay and a recursive regularization to guarantee high network tracking accuracy, temporal precision and robustness to noise. To validate the proposed algorithm, we performed an extensive comparison against the classical Kalman filter, in both realistic surrogate networks and real electroencephalography (EEG) data. In both simulations and real data, we show that the STOK filter estimates time-frequency patterns of directed connectivity with significantly superior performance. The advantages of the STOK filter were even clearer in real EEG data, where the algorithm recovered latent structures of dynamic connectivity from epicranial EEG recordings in rats and human visual evoked potentials, in excellent agreement with known physiology. These results establish the STOK filter as a powerful tool for modeling dynamic network structures in biological systems, with the potential to yield new insights into the rapid evolution of network states from which brain functions emerge.

In the last decade, the use of high-density electrode arrays for EEG recordings combined with the improvements of source reconstruction algorithms has allowed the investigation of brain networks dynamics at a sub-second scale. One powerful tool for investigating large-scale functional brain networks with EEG is timevarying effective connectivity applied to source signals obtained from electric source imaging. Due to computational and interpretation limitations, the brain is usually parcelled into a limited number of regions of interests (ROIs) before computing EEG connectivity. One specific need and still open problem is how to represent the time-and frequency-content carried by hundreds of dipoles with diverging orientation in each ROI with one unique representative time-series. The main aim of this paper is to provide a method to compute a signal that This is a pre-print of an article published in Brain Topography.

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Highlights Abnormal brain network is visible in absence of epileptic activity on hd-EEG. Higher efficiency in patients vs. controls in absence of epileptic activity on hd-EEG. Resting state networks are altered in epileptic patients.

Previous literature has demonstrated that hypoglycemic events in patients with type 1 diabetes (T1D) are associated with measurable scalp electroencephalography (EEG) changes in power spectral density. In the present study, we used a dataset of 19-channel scalp EEG recordings in 34 patients with T1D who underwent a hyperinsulinemic–hypoglycemic clamp study. We found that hypoglycemic events are also characterized by EEG complexity changes that are quantifiable at the single-channel level through empirical conditional and permutation entropy and fractal dimension indices, i.e., the Higuchi index, residuals, and tortuosity. Moreover, we demonstrated that the EEG complexity indices computed in parallel in more than one channel can be used as the input for a neural network aimed at identifying hypoglycemia and euglycemia. The accuracy was about 90%, suggesting that nonlinear indices applied to EEG signals might be useful in revealing hypoglycemic events from EEG recordings in patients with T1D.