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Seri, S.

Publications and source records attributed to Seri, S..

3 recordsLinked to original sources

Entropy of the resting state cortex in epilepsy

BackgroundEpilepsy has long been conceptualised as a disorder in which aberrant brain dynamics extend beyond the epileptogenic zone. Evidence demonstrates that loss of entropy is a generic feature of pathological dynamics in the brain, including the ictal state. However, the impact of recurrent seizures on entropy in the interictal state remains unknown. MethodsResting state magnetoencephalography (MEG) scans and resection masks of 32 individuals with epilepsy who had Engel I outcome post-surgery were retrospectively retrieved. Using co-registered FreeSurfer parcellations, we reconstructed the source localised MEG time series and computed sample entropy for 114 regions of interest. We then tested the association of entropy with the resected volume of the brain, and additional clinical variables including the age of seizure onset, seizure frequency and duration of epilepsy. To further understand the temporal relationship between seizure onset and entropy in the interictal state, we collected and computed sample entropy for week-long EEG traces from leucine-rich glioma inactivated 1 monoclonal antibody (LGI1-mAb) rodent models of autoimmune encephalitis (n=5) and control rats (n=5). ResultsIn individuals with epilepsy, a lower age of seizure onset was associated with lower mean sample entropy of the whole cortex (Spearmans rho =0.60, p<0.001; partial correlation =0.41, p=0.021). Entropy did not differ between the resected and non-resected regions of the brain. Furthermore, LGI1-mAb treated rodents showed a persistent decrease in sample entropy as compared to control rats, after the onset of seizures, and this difference was greatest during periods of highest seizure frequency (p<0.001). ConclusionRecurrent seizures are associated with a persistent decrease in entropy, even in the interictal state, and this decrease was found to be most profound and affecting the whole cortex in patients who had a lower age of seizure onset.

neuroscience↗

Dynamic Resting-State EEG Alpha Connectivity: Quantifying Brain Network State Evolution in Individuals with Psychosis

This study investigates brain dynamic connectivity patterns in psychosis and their relationship with psychopathological profile and cognitive functioning using a novel dynamic connectivity pipeline on resting-state EEG. Data from seventy-eight individuals with first-episode psychosis (FEP) and sixty control subjects (CTR) were analyzed. Source estimation was performed using eLORETA, and connectivity matrices in the alpha band were computed with the weighted phase-lag index. A modified k-means algorithm was employed to cluster connectivity matrices into distinct brain network states (BNS), from which metrics were extracted. The segmentation revealed five distinct BNSs. FEP exhibited significantly lower connectivity power in BNS 2 and 5 and a greater duration dispersion in BNS 1 than CTR. Negative correlations were identified between BNS metrics and negative symptoms in FEP. In CTR, correlations were found between BNS metrics and cognitive domains. This analysis method highlights the variability of neural dynamics in psychosis and their relationship with negative symptoms.

neuroscience↗

The impact of paediatric epilepsy and co-occurring neurodevelopmental disorders on functional brain networks in wake and sleep

Epilepsy is one of the most common neurological disorders in children. Diagnosing epilepsy in children can be very challenging, especially as it often coexists with neurodevelopmental conditions like autism and ADHD. Functional brain networks obtained from neuroimaging and electrophysiological data in wakefulness and sleep have been shown to contain signatures of neurological disorders, and can potentially support the diagnosis and management of co-occurring neurodevelopmental conditions. In this work, we use electroencephalography (EEG) recordings from children, in restful wakefulness and sleep, to extract functional connectivity networks in different frequency bands. We explore the relationship of these networks with epilepsy diagnosis and with measures of neurodevelopmental traits, obtained from questionnaires used as screening tools for autism and ADHD. We explore differences in network markers between children with and without epilepsy in wake and sleep, and quantify the correlation between such markers and measures of neurodevelopmental traits. Our findings highlight the importance of considering the interplay between epilepsy and neurodevelopmental traits when exploring network markers of epilepsy.

neuroscience↗