We aimed to compare network properties between focal-onset nonconvulsive status epilepticus (NCSE) and toxic/metabolic encephalopathy (TME) during periods of periodic discharge using graph theoretical analysis, and to evaluate the applicability of graph measures as markers for the differential diagnosis between focal-onset NCSE and TME, using machine learning algorithms. Electroencephalography (EEG) data from 50 focal-onset NCSE and 44 TMEs were analyzed. Epochs with nonictal periodic discharges were selected, and the coherence in each frequency band was analyzed. Graph theoretical analysis was performed to compare brain network properties between the groups. Eight different traditional machine learning methods were implemented to evaluate the utility of graph theoretical measures as input features to discriminate between the two conditions. The average degree (in delta, alpha, beta, and gamma bands), strength (in delta band), global efficiency (in delta and alpha bands), local efficiency (in delta band), clustering coefficient (in delta band), and transitivity (in delta band) were higher in TME than in NCSE. TME showed lower modularity (in delta band) and assortativity (in alpha, beta, and gamma bands) than NCSE. Machine learning algorithms based on EEG global graph measures classified NCSE and TME with high accuracy, and gradient boosting was the most accurate classification model with an area under the receiver operating characteristics curve of 0.904. Our findings on differences in network properties may provide novel insights that graph measures reflecting the network properties could be quantitative markers for the differential diagnosis between focal-onset NCSE and TME.

We compared neural activities and network properties between the antihistamine-induced seizures (AIS) and seizure-free groups, with the hypothesis that patients with AIS might have inherently increased neural activities and network properties that are easily synchronized. Resting-state electroencephalography (EEG) data were collected from 27 AIS patients and 30 healthy adults who had never had a seizure. Power spectral density analysis was used to compare neural activities in each localized region. Functional connectivity (FC) was measured using coherence, and graph theoretical analyses were performed to compare network properties between the groups. Machine learning algorithms were applied using measurements found to be different between the groups in the EEG analyses as input features. Compared with the seizure-free group, the AIS group showed a higher spectral power in the entire regions of the delta, theta, and beta bands, as well as in the frontal areas of the alpha band. The AIS group had a higher overall FC strength, as well as a shorter characteristic path length in the theta band and higher global efficiency, local efficiency, and clustering coefficient in the beta band than the seizure-free group. The Support Vector Machine, k-Nearest Neighbor, and Random Forest models distinguished the AIS group from the seizure-free group with a high accuracy of more than 99%. The AIS group had seizure susceptibility considering both regional neural activities and functional network properties. Our findings provide insights into the underlying pathophysiological mechanisms of AIS and may be useful for the differential diagnosis of new-onset seizures in the clinical setting.

We aimed to identify structural and functional changes in healthy adults with catch‐up sleep (CUS), we applied seed‐based functional connectivity (FC) analysis using resting‐state functional magnetic resonance imaging (MRI). We hypothesized that deficits in reward processing could be a fundamental mechanism underlying the motivation of taking CUS. Then, 55 healthy adults voluntarily (34 with CUS and 21 without CUS) participated in this study. Voxel‐based morphometry was performed to explore region of gray matter volume (GMV) difference between CUS and non‐CUS groups. Between‐group comparison of FC was then carried out using resting‐state functional MRI analysis seeding at the region of volume difference. Moreover, the region of volume difference and the strength of FC were correlated with scores of questionnaires for reward‐seeking behavior and clinical variables. CUS group had a higher reward‐seeking tendency, and increased GMV in the bilateral nucleus accumbens and right superior frontal gyrus relative to non‐CUS group. FC analysis seeding at the bilateral accumbens revealed increases of FC in the bilateral medial prefrontal cortex in CUS group compared to non‐CUS group. The questionnaire scores reflecting the reward‐seeking tendency were correlated with the FC strength between bilateral accumbens and medial prefrontal cortex. Our results indicate that CUS is associated with reward‐seeking tendency and increased GMV and FC in regions responsible for reward network. Our findings suggest that enhanced reward network could be the crucial mechanism underlying taking CUS and might be implicated in the detrimental effects of circadian misalignment.