This study introduces a framework for the information-theoretic analysis of brain functional connectivity performed at the level of electroencephalogram (EEG) sources. The framework combines the use of common spatial patterns to select the EEG components which maximize the variance between two experimental conditions, simultaneous implementation of vector autoregressive modeling (VAR) with independent component analysis to describe the joint source dynamics and their projection to the scalp, and computation of information dynamics measures (information storage, information transfer, statistically significant network links) from the source VAR parameters. The proposed framework was tested on simulated EEGs obtained mixing source signals generated under different coupling conditions, showing its ability to retrieve source information dynamics from the scalp signals. Then, it was applied to investigate scalp and source brain connectivity in a group of children manifesting episodes of focal and generalized epilepsy; the analysis was performed on EEG signals lasting 5 s, collected in two consecutive windows preceding and one window following each ictal episode. Our results show that generalized seizures are associated with a significant decrease from pre-ictal to post-ictal periods of the information stored in the signals and of the information transferred among them, reflecting reduced self-predictability and causal connectivity at the level of both scalp and source brain dynamics. On the contrary, in the case of focal seizures the scalp EEG activity was not discriminated across conditions by any information measure, while source analysis revealed a tendency of the measures of information transfer to increase just before seizures and to decrease just after seizures. These results suggest that focal epileptic seizures are associated with a reorganization of the topology of EEG brain networks which is only visible analyzing connectivity among the brain sources. Our findings emphasize the importance of EEG modeling approaches able to deal with the adverse effects of volume conduction on brain connectivity analysis, and their potential relevance to the development of strategies for prediction and clinical treatment of epilepsy.

In this work, partial information decomposition (PID) was applied to the time series of heart rate and EEG amplitude variability to investigate the dynamical interactions in brain-heart coupling before and after epileptic seizures. From ECG and EEG signals collected on 23 children suffering from focal epilepsy, the RR intervals and the EEG variance at ipsilateral and contralateral temporal electrodes were computed in four different time windows before and after the seizures. Static PID was used to obtain redundant, unique and synergistic components of the total information shared between the series of RR and EEG variance. Results highlight, in the progression from preictal to postictal states, a statistically significant change of mutual information at the ipsilateral electrode and of the synergy between brain locations.

Objective: The ongoing conflict in Ukraine has created a severe humanitarian crisis, disproportionately affecting vulnerable populations such as children with chronic conditions. We set out to determine information about mental health in children with epilepsy in Ukraine affected by the conflict. Methods: Mental health outcomes in 213 Ukrainian children with epilepsy during the conflict were screened using standardized instruments for post‐traumatic stress disorder (PTSD; Harvard Trauma Questionnaire), anxiety (7 item Generalized Anxiety Disorder module, GAD‐7), depression (Patient Health Questionnaire‐9, PHQ‐9), and epilepsy severity (Global Assessment of Severity of Epilepsy scale, GASE). Data were collected between February and June 2023 through self‐ or proxy‐completed surveys. Results: Participants (14.6%) screened positive for PTSD symptoms, with girls significantly more affected than boys. A high proportion screened positive for symptoms of anxiety and depression, with 22% in the range for severe anxiety and 55% in the moderate‐to‐severe depression range. Epilepsy severity strongly correlated with mental health burden. Access to care was severely disrupted: 76% reported difficulty obtaining antiseizure medications, and 51% struggled to access medical services, contributing to increased seizure frequency in 56% of cases. Regression analyses identified epilepsy severity and barriers to medical care as key predictors of adverse mental health outcomes. Significance: These findings underscore the compounded impact of war and chronic illness on children's psychological well‐being and highlight the urgent need for trauma‐informed, multilevel interventions, and improved access to health care for this vulnerable population.

Pathogenic variants in the PAK1 gene are linked to neurodevelopmental and neurodegenerative disorders by disrupting neuronal signaling and function. Despite increasing recognition, the mechanisms underlying these conditions remain incompletely understood, limiting therapeutic options. Here, we report a novel de novo PAK1 variant, c.396C>A (p.Asn132Lys), in a 5-year-old girl with Intellectual Developmental Disorder with Macrocephaly, Seizures, and Speech Delay (IDDMSSD). The patient presented with mild intellectual disability, delayed speech, macrocephaly, hypotonia, gait ataxia, autism-like behaviors, and focal epileptiform activity. Trio exome sequencing confirmed the variant as likely pathogenic, absent in her parents and population databases. This finding expands the phenotypic spectrum of PAK1-related disorders and underscores the critical role of the autoinhibitory domain in neurodevelopment. In addition, we performed a comprehensive literature review of PAK1 variants affecting both the autoregulatory and kinase domains, summarizing associated clinical features and pathogenic mechanisms. Our study highlights the importance of identifying PAK1 pathogenic variants for accurate diagnosis, refined genotype-phenotype correlations, and the development of potential targeted therapeutic strategies. By integrating novel case data with existing literature, this work advances understanding of PAK1-related neurodevelopmental disorders and supports the application of genetic analysis in rare pediatric NDD cases.