Rett syndrome is a disease that involves acute cognitive impairment and, consequently, a complex and varied symptomatology. This study evaluates the EEG signals of twenty-nine patients and classify them according to the level of movement artifact. The main goal is to achieve an artifact rejection strategy that performs well in all signals, regardless of the artifact level. Two different methods have been studied: one based on the data distribution and the other based on the energy function, with entropy as its main component. The method based on the data distribution shows poor performance with signals containing high amplitude outliers. On the contrary, the method based on the energy function is more robust to outliers. As it does not depend on the data distribution, it is not affected by artifactual events. A double rejection strategy has been chosen, first on a motion signal (accelerometer or EEG low-pass filtered between 1 and 10 Hz) and then on the EEG signal. The results showed a higher performance when working combining both artifact rejection methods. The energy-based method, to isolate motion artifacts, and the data-distribution-based method, to eliminate the remaining lower amplitude artifacts were used. In conclusion, a new method that proves to be robust for all types of signals is designed.

Functional connectivity is scarcely studied in Rett syndrome (RTT). Explorations revealed associations between RTT's clinical, genetic profiles, and coherence measures, highlighting an unexplored frontier in understanding RTT's neural mechanisms and cognitive processes. To evaluate the effects of diverse cognitive stimulations-learning-focused versus gaming-oriented-on electroencephalography brain connectivity in RTT. The comparison with resting states aimed to uncover potential biomarkers and insights into the neural processes associated with RTT. The study included 15 girls diagnosed with RTT. Throughout sessions lasting about 25 min, participants alternated between active and passive tasks, using an eyetracker device while their brain activity was recorded with a 20-channel EEG. Results revealed significant alterations during cognitive tasks, notably in delta, alpha and beta bands. Both tasks induced spectral pattern changes and connectivity shifts, hinting at enhanced neural processing. Hemispheric asymmetry decreased during tasks, suggesting more balanced neural processing. Linear and nonlinear connectivity alterations were observed in active tasks compared to resting state, while passive tasks showed no significant changes. Results underscores the potential of cognitive stimulation for heightened cognitive abilities, promoting enhanced brain connectivity and information flow in Rett syndrome. These findings offer valuable markers for evaluating cognitive interventions and suggest gaming-related activities as effective tools for improving learning outcomes.

Objective. Epilepsy affects around 50 million people worldwide, and reliable pre-seizure biomarkers could significantly improve neuromodulation therapies for drug-resistant patients. Recent research using stereo-electroencephalography (sEEG) has revealed transient changes in network dynamics preceding seizures. In particular, our previous work showed that these alterations are driven by recurrent, short-lasting (0.6 s) high-connectivity network configurations—termed high-connectivity states (HCSs). Here, we aim to replicate and further characterize HCS as a biomarker in a multicentric patient cohort, assess its robustness across recording modalities and montages, explore its relationship with interpretable physiological variables, and examine its network-level association with seizure-onset zone (SOZ) dynamics. Approach. We analyzed long-term intracranial EEG recordings from 12 patients with sEEG and electrocorticography. In two patients with extensive clinical information, we examined the interplay between HCS and SOZ dynamics. We also developed a low-dimensional stochastic network model to investigate mechanistic rationales of HCS emergence. Additionally, we compared HCS dynamics with gamma-band activity and heart rate, and tested robustness across different montage configurations. Main Results. In most patients, HCS probability reliably increased hours before seizure onset. In the two deeply characterized patients, this increase was specifically linked to an increased network centrality within the SOZ. The network model revealed that changes in HCS probability stem primarily from topological reconfigurations rather than changes in mean connectivity, underscoring the importance of dynamic interactions between epileptogenic and non-epileptogenic regions. Significance. These results support HCS probability as a promising biomarker for early seizure prediction and offer mechanistic insights into pre-seizure brain network dynamics.