Dynamic directed interictal connectivity in left and right temporal lobe epilepsy

Coito, Ana; Plomp, Gijs; Genetti, M.; Abela, Eugenio; Wiest, Roland; Seeck, Margitta; Michel, Christoph M.; Vulliémoz, Serge

doi:10.1111/epi.12904

Cited by 128 publications

(137 citation statements)

References 47 publications

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“…We subtracted the number of contralateral altered connections (CAC), from the number of ipsilateral altered connections (IAC) and divided it by the total number of altered connections [Coito et al, 2015]:

normalL normalI = \frac{normalI normalA normalC ‐ normalC normalA normalC}{normalI normalA normalC + normalC normalA normalC}

…”

Section: Methodsmentioning

confidence: 99%

Large‐scale brain networks are distinctly affected in right and left mesial temporal lobe epilepsy

Campos

Coan

Yasuda

et al. 2016

Human Brain Mapping

108

102

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Mesial temporal lobe epilepsy (MTLE) with hippocampus sclerosis (HS) is associated with functional and structural alterations extending beyond the temporal regions and abnormal pattern of brain resting state networks (RSNs) connectivity. We hypothesized that the interaction of large‐scale RSNs is differently affected in patients with right‐ and left‐MTLE with HS compared to controls. We aimed to determine and characterize these alterations through the analysis of 12 RSNs, functionally parceled in 70 regions of interest (ROIs), from resting‐state functional‐MRIs of 99 subjects (52 controls, 26 right‐ and 21 left‐MTLE patients with HS). Image preprocessing and statistical analysis were performed using UF2C‐toolbox, which provided ROI‐wise results for intranetwork and internetwork connectivity. Intranetwork abnormalities were observed in the dorsal default mode network (DMN) in both groups of patients and in the posterior salience network in right‐MTLE. Both groups showed abnormal correlation between the dorsal‐DMN and the posterior salience, as well as between the dorsal‐DMN and the executive‐control network. Patients with left‐MTLE also showed reduced correlation between the dorsal‐DMN and visuospatial network and increased correlation between bilateral thalamus and the posterior salience network. The ipsilateral hippocampus stood out as a central area of abnormalities. Alterations on left‐MTLE expressed a low cluster coefficient, whereas the altered connections on right‐MTLE showed low cluster coefficient in the DMN but high in the posterior salience regions. Both right‐ and left‐MTLE patients with HS have widespread abnormal interactions of large‐scale brain networks; however, all parameters evaluated indicate that left‐MTLE has a more intricate bihemispheric dysfunction compared to right‐MTLE. Hum Brain Mapp 37:3137–3152, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

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normalL normalI = \frac{normalI normalA normalC ‐ normalC normalA normalC}{normalI normalA normalC + normalC normalA normalC}

…”

Section: Methodsmentioning

confidence: 99%

Large‐scale brain networks are distinctly affected in right and left mesial temporal lobe epilepsy

Campos

Coan

Yasuda

et al. 2016

Human Brain Mapping

108

102

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show abstract

“…In particular, there is a growing interest in Granger Causality measures (Bressler et al , 2011) that assess whether incorporating the past from a signal from another channel improves the prediction of the signal in a channel of interest. Thus, several variants of connectivity have been applied to epilepsy (Amini et al , 2011, Epstein et al , 2014, Coito et al , 2015. It is interesting to note that Granger causality as implemented with auto-regressive models (Brovelli et al , 2004) or with a covariance-based approach (Brovelli et al , 2015) may not be radically different from a classical correlation analysis (Davey et al , 2013).…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Graph Measures of Node Strength for Characterizing Preictal Synchrony in Partial Epilepsy

et al. 2016

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The reference electrophysiological pattern at seizure onset is the "rapid discharge," as visible on intracerebral electroencephalography (EEG). This discharge typically corresponds to a decrease of synchrony across brain areas. In contrast, the preictal period can exhibit patterns of increased synchrony, which can be quantified by network measures. Our objective was to compare preictal synchrony with a quantification of the rapid discharge as provided by the epileptogenicity index (EI). We investigated 24 seizures from 12 patients recorded by stereotaxic EEG (SEEG). Seizures were classified visually as containing preictal synchrony or not. We computed pairwise nonlinear correlation (h(2)) across channels in the 8 sec preceding the rapid discharge. The sum of ingoing and outgoing links (IN and OUT node strength), as well as the sum of all links (total strength, TOT) were computed for each region. We tested several filtering schemes, and quantified the capacity of each strength measure to serve as a detector of regions with high EI values using a receiver operating characteristic (ROC) analysis. We found that the best correspondence between node strength and EI was obtained for the OUT and TOT measures, for signals filtered in the 15-40 Hz band-that is, for the band corresponding to the spiky part of epileptic discharges. In agreement with these results, we also found that the ROC results were improved when considering only seizures with visible synchronous patterns in the preictal period. Our results suggest that measuring strength of preictal connectivity graphs can bring useful clinical information on the epileptogenic zone.

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“…Using ESI of interictal spikes and a timevarying frequency-resolved effective connectivity metric, Coito et al found dissociation in the pattern of connectivity between patients with left and right TLE (Coito et al, 2015). Indeed, the latter exhibited increased contralateral connectivity, in line with contralateral frontal functional deficits in right TLE.…”

Section: Eeg/meg Connectivitymentioning

confidence: 99%

Multimodal investigation of epileptic networks

Zerouali

Ghaziri

Nguyen

2016

Progress in Brain Research

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Dynamic directed interictal connectivity in left and right temporal lobe epilepsy

Cited by 128 publications

References 47 publications

Large‐scale brain networks are distinctly affected in right and left mesial temporal lobe epilepsy

Large‐scale brain networks are distinctly affected in right and left mesial temporal lobe epilepsy

Graph Measures of Node Strength for Characterizing Preictal Synchrony in Partial Epilepsy

Multimodal investigation of epileptic networks

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