Dynamic brain network states in human generalized spike-wave discharges

Tangwiriyasakul, Chayanin; Perani, Suejen; Centeno, María; Yaakub, Siti N.; Abela, Eugenio; Carmichael, David W.; Richardson, Mark P.

doi:10.1093/brain/awy223

Cited by 62 publications

(70 citation statements)

References 62 publications

(104 reference statements)

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“…8 We observed three canonical networks in these data: (1) a network prominent during GSW in patients (GSW network), (2) a network prominent prior to GSW in patients (sensorimotor network), and (3) a network prominent in healthy controls at the time of random events (occipital network; see Table S2 for a list of brain regions included in each network). 8 We observed three canonical networks in these data: (1) a network prominent during GSW in patients (GSW network), (2) a network prominent prior to GSW in patients (sensorimotor network), and (3) a network prominent in healthy controls at the time of random events (occipital network; see Table S2 for a list of brain regions included in each network).…”

Section: Estimation Of Average Network Synchronymentioning

confidence: 91%

“…We have previously acquired and published EEG fMRI data from 21 patients diagnosed with juvenile myoclonic epilepsy (JME) or generalized tonic-clonic seizures only (GTCSO) 8 ; here, we include the 13 in whom fMRI runs were entirely free of GSW (mean age = 20.5 ± SD 6.6 years), in addition to 18 healthy controls reported in the same prior study (mean age = 23.9 ± SD 3.8 years), and 17 unaffected first-degree relatives collected during the same period of time, but not previously reported (mean age = 39.4 ± SD 14.2 years; see Table S1). Note that none of the relatives was related to any of the patients in this study.…”

Section: Participantsmentioning

confidence: 99%

“…8 In summary, BOLD signals were first bandpass filtered between 0.04 and 0.07 Hz. 8 In summary, BOLD signals were first bandpass filtered between 0.04 and 0.07 Hz.…”

Section: Fmri Data Analysismentioning

confidence: 99%

“…12 We then parcellated the brain into 90 regions using automatic anatomical labeling. 8,15 Tensor decomposition was applied to the series of adjacency matrices for each run to try to reduce the number of spurious network connections. 14 Next, the Hilbert transform was applied to estimate the instantaneous phase of the first principal component in each region.…”

Section: Fmri Data Analysismentioning

confidence: 99%

“…For example, we found enhanced electroencephalographic (EEG) network synchrony in patients with GGE and unaffected first-degree relatives. 6,7 In a recent study, we found that synchrony in specific networks, observed with blood oxygen level-dependent (BOLD) fMRI and simultaneous EEG, varies dynamically around the time of generalized spike-wave (GSW) events observed in EEG, 8 apparently anticipating the onset of GSW by several seconds. 4,5 An endophenotype is a heritable trait that is a component of a disorder or associated with high liability to develop the disorder.…”

Section: Introductionmentioning

confidence: 99%

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Sensorimotor network hypersynchrony as an endophenotype in families with genetic generalized epilepsy: A resting‐state functional magnetic resonance imaging study

et al. 2019

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Summary Recent evidence suggests that three specific brain networks show state‐dependent levels of synchronization before, during, and after episodes of generalized spike‐wave discharges (GSW) in patients with genetic generalized epilepsy (GGE). Here, we investigate whether synchronization in these networks differs between patients with GGE (n = 13), their unaffected first‐degree relatives (n = 17), and healthy controls (n = 18). All subjects underwent two 10‐minute simultaneous electroencephalographic–functional magnetic resonance imaging (fMRI) recordings without GSW. Whole‐brain data were divided into 90 regions, and blood oxygen level–dependent (BOLD) phase synchrony in a 0.04–0.07‐Hz band was estimated between all pairs of regions. Three networks were defined: (1) the network with highest synchrony during GSW events, (2) a sensorimotor network, and (3) an occipital network. Average synchrony (mean node degree) was inferred across each network over time. Notably, synchrony was significantly higher in the sensorimotor network in patients and in unaffected relatives, compared to controls. There was a trend toward higher synchrony in the GSW network in patients and in unaffected relatives. There was no difference between groups for the occipital network. Our findings provide evidence that elevated fMRI BOLD synchrony in a sensorimotor network is a state‐independent endophenotype of GGE, present in patients in the absence of GSW, and present in unaffected relatives.

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Section: Estimation Of Average Network Synchronymentioning

confidence: 91%

Section: Participantsmentioning

confidence: 99%

“…8 In summary, BOLD signals were first bandpass filtered between 0.04 and 0.07 Hz. 8 In summary, BOLD signals were first bandpass filtered between 0.04 and 0.07 Hz.…”

Section: Fmri Data Analysismentioning

confidence: 99%

Section: Fmri Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sensorimotor network hypersynchrony as an endophenotype in families with genetic generalized epilepsy: A resting‐state functional magnetic resonance imaging study

et al. 2019

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The role of the primary sensorimotor system in generalized epilepsy: Evidence from the cerebello–cerebral functional integration

Chen,

Jiang,

et al. 2023

Human Brain Mapping

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The interaction between cerebellum and cerebrum participates widely in function from motor processing to high‐level cognitive and affective processing. Because of the motor symptom, idiopathic generalized epilepsy (IGE) patients with generalized tonic–clonic seizure have been recognized to associate with motor abnormalities, but the functional interaction in the cerebello–cerebral circuit is still poorly understood. Resting‐state functional magnetic resonance imaging data were collected for 101 IGE patients and 106 healthy controls. The voxel‐based functional connectivity (FC) between cerebral cortex and the cerebellum was contacted. The functional gradient and independent components analysis were applied to evaluate cerebello–cerebral functional integration on the voxel‐based FC. Cerebellar motor components were further linked to cerebellar gradient. Results revealed cerebellar motor functional modules were closely related to cerebral motor components. The altered mapping of cerebral motor components to cerebellum was observed in motor module in patients with IGE. In addition, patients also showed compression in cerebello–cerebral functional gradient between motor and cognition modules. Interestingly, the contribution of the motor components to the gradient was unbalanced between bilateral primary sensorimotor components in patients: the increase was observed in cerebellar cognitive module for the dominant hemisphere primary sensorimotor, but the decrease was found in the cerebellar cognitive module for the nondominant hemisphere primary sensorimotor. The present findings suggest that the cerebral primary motor system affects the hierarchical architecture of cerebellum, and substantially contributes to the functional integration evidence to understand the motor functional abnormality in IGE patients.

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