Different Frequency Bands in Various Regions of the Brain Play Different Roles in the Onset and Wake-Sleep Stages of Infantile Spasms

Dong, Yan; Xu, Ruijuan; Zhang, Yaodong; Shi, Yonghui; Du, Kaixian; Jia, Tianming; Wang, Jun; Wang, Fang

doi:10.3389/fped.2022.878099

Cited by 3 publications

(7 citation statements)

References 32 publications

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“…This builds upon previous studies that explore connectivity in IS, 29,30 and relation of sleep-wake transitions in IS. [31][32][33] Our finding that IS are associated with increased HFO connectivity is supported by previous studies that identify both the presence of ictal HFOs, 6,34,35 and their coherence 8 as biomarkers for epilepsy. By comparing pre-and post-onset, we found that the fast ripple networks may be more specific to the behavioral onset.…”

Section: Discussionsupporting

confidence: 87%

“…The anteroposterior gradient was also observed in arousals and awake states in controls suggesting that arousal networks might be related to the formulation of spasm‐related anteroposterior gradient in fast ripple hub connectivity. This builds upon previous studies that explore connectivity in IS, 29,30 and relation of sleep–wake transitions in IS 31–33 …”

Section: Discussionmentioning

confidence: 77%

“…This builds upon previous studies that explore connectivity in IS, 29 , 30 and relation of sleep–wake transitions in IS. 31 , 32 , 33 …”

Section: Discussionmentioning

confidence: 99%

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EEG‐based spatiotemporal dynamics of fast ripple networks and hubs in infantile epileptic spasms

Samfira,

Galanopoulou,

Nariai

et al. 2023

Epilepsia Open

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ObjectiveInfantile epileptic spasms (IS) are epileptic seizures that are associated with increased risk for developmental impairments, adult epilepsies, and mortality. Here, we investigated coherence‐based network dynamics in scalp EEG of infants with IS to identify frequency‐dependent networks associated with spasms. We hypothesized that there is a network of increased fast ripple connectivity during the electrographic onset of clinical spasms, which is distinct from controls.MethodsWe retrospectively analyzed peri‐ictal and interictal EEG recordings of 14 IS patients. The data was compared with 9 age‐matched controls. Wavelet phase coherence (WPC) was computed between 0.2‐400 Hz. Frequency‐ and time‐dependent brain networks were constructed using this coherence as the strength of connection between two EEG channels, based on graph theory principles. Connectivity was evaluated through global efficiency (GE) and channel‐based closeness centrality (CC), over frequency and time.ResultsGE in the fast ripple band (251‐400 Hz) was significantly greater following onset of spasms in all patients (p < 0.05). Fast ripple networks during the first 10s from spasm onset show enhanced anteroposterior gradient in connectivity (posterior > central > anterior, Kruskal‐Wallis p < 0.001), with maximum CC over the centroparietal channels in 10/14 patients. Additionally, this anteroposterior gradient in CC connectivity is observed during spasms but not during the interictal awake or asleep states of infants with IS. In controls, anteroposterior gradient in fast ripple CC was noted during arousals and wakefulness but not during sleep. There was also a simultaneous decrease in GE in the 5‐8 Hz range after onset of spasms (p < 0.05), of unclear biological significance.SignificanceWe identified an anteroposterior gradient in CC connectivity of fast ripple hubs during spasms. This anteroposterior gradient observed during spasms is similar to the anteroposterior gradient in CC connectivity observed in wakefulness or arousals in controls, suggesting that this state change is related to arousal networks.

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Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 77%

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EEG‐based spatiotemporal dynamics of fast ripple networks and hubs in infantile epileptic spasms

Samfira,

Galanopoulou,

Nariai

et al. 2023

Epilepsia Open

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“…Detailed introduction to the BFN of patients with IESS, based on graph theory combined with Weighted Phase Lag Index (wPLI), can be found in our previous study (12) and Supplementary Table S1. CPL represents the average path length of all nodes in the network, indicating the information transmission capability of the whole-brain network.…”

Section: Eeg Data Acquisition and Preprocessingmentioning

confidence: 99%

“…High-frequency scalp oscillations have proven to be efficacious biomarkers for gauging the effectiveness and prognostic capacity of ACTH therapy in IESS patients (7). The transmission capacity of fast waves, defensive prowess, and local connectivity of slow waves all manifested an enhancement during IESS ictal onset (12). Nevertheless, the exploration of BFN alterations in children diagnosed with IESS, contrasted with healthy infants, remains inadequate.…”

Section: Introductionmentioning

confidence: 99%

Crucial involvement of fast waves and Delta band in the brain network attributes of infantile epileptic spasms syndrome

Dong,

Jin,

et al. 2023

Front. Pediatr.

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ObjectiveThis study aims to describe the characteristics of the brain network attributes in children diagnosed with Infantile Epileptic Spasms Syndrome (IESS) and to determine the influence exerted by adrenocorticotrophic hormone (ACTH) or methylprednisolone (MP) on network attributes.MethodsIn this retrospective cohort study, we recruited 19 infants diagnosed with IESS and 10 healthy subjects as the control from the Pediatric Neurology Department at the Third Affiliated Hospital of Zhengzhou University between October 2019 and December 2020. The first thirty-minute processed electroencephalograms (EEGs) were clipped and filtered into EEG frequency bands (2 s each). A comparative assessment was conducted between the IESS group and the controls as well as the pre- and post-treatment in the IESS group. Mutual information values for each EEG channel were collected and compared including characteristic path length (CPL), node degree (ND), clustering coefficient (CC), and betweenness centrality (BC), based on graph theory.ResultsComparing the control group, in the IESS group, there was an increase in CPL of the Delta band, and a decrease in ND and CC of the Delta band during the waking period, contrary to those during the sleeping period (P < 0.05), a decreased in CPL of the fast waves and an increase in ND and CC (P < 0.05) in the sleep-wake cycle, and a decrease in ND and CC of the Theta band in the waking phase. Post-treatment compared with the pre-treatment, during the waking ictal phase, there was a noted decrease in CPL in the Delta band and fast waves, while an increase was observed in ND and CC (P < 0.05).ConclusionsThe Delta band and fast waves are crucial components of the network attributes in IESS.SignificanceThis investigation provides a precise characterization of the brain network in children afflicted with IESS, and lays the groundwork for predicting the prognosis using graph theory.

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Predictive modeling based on functional connectivity of interictal scalp EEG for infantile epileptic spasms syndrome

Kanai,

Oguri,

Okanishi

et al. 2024

Clinical Neurophysiology

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Different Frequency Bands in Various Regions of the Brain Play Different Roles in the Onset and Wake-Sleep Stages of Infantile Spasms

Cited by 3 publications

References 32 publications

EEG‐based spatiotemporal dynamics of fast ripple networks and hubs in infantile epileptic spasms

EEG‐based spatiotemporal dynamics of fast ripple networks and hubs in infantile epileptic spasms

Crucial involvement of fast waves and Delta band in the brain network attributes of infantile epileptic spasms syndrome

Predictive modeling based on functional connectivity of interictal scalp EEG for infantile epileptic spasms syndrome

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