Disruption of functional gradient in patients with genetic generalized epilepsy: evidence from hdEEG
Poster No:
1173
Submission Type:
Abstract Submission
Authors:
Juanli Zhang1, Isotta Rigoni1, Dimitri Van De Ville1, Serge Vulliémoz1, Nicolas Roehri1
Institutions:
1University of Geneva, Geneva, Switzerland
First Author:
Co-Author(s):
Introduction:
Genetic Generalized Epilepsy (GGE) represents around 20% of adult epilepsies (Vorderwülbecke et al., 2022). It is recognized as a network disorder involving both hemispheres and multiple brain regions. Previous studies have demonstrated that GGE is accompanied by abnormal activity in multiple functional networks and communication between them (Wang et al., 2011; Yang et al., 2018). However, it remains elusive how the functional hierarchical positioning of the networks is represented in the GGE. The goal of this study is to use the approach of functional gradient, which has been well established in fMRI studies capturing the similarity of connectivity profiles across regions (Margulies et al., 2016), to investigate the pattern of gradual ordering of the networks in GGE, and its relation to clinical parameters.
Methods:
We analyzed a high-density EEG dataset (256 electrodes) from a group of patients diagnosed with GGE (N = 20) and sex- and age-matched healthy controls recruited at the University Hospitals of Geneva (ethics protocol #2020-02526). Source reconstructed activity was obtained by inverse modeling using individual head model and the eLORETA algorithm (Pascual-Marqui et al., 2007), and 450 regions of interest based on the 4th scale of Lausanne Atlas (Hagmann et al., 2008). Functional connectivity was computed on the resting state data free of interictal epileptiform discharges by applying the debiased weighted phase lag index to pairs of regions. We computed similarity matrix based on cosine similarity and projected them into low-dimensional space using diffusion map embedding, which results in a set of principal eigenvectors depicting modes of largest variance. This low-dimensional representation reveals a set of gradients (Vos de Wale et al., 2020), and we focused on the first two gradient dimensions derived from the functional connectomes obtained from different frequency bands (delta to beta). Gradient scores were then summarized into the subnetwork level according to the canonical Yeo 7 network partition scheme (Yeo et al., 2011).
Results:
Our data revealed that, compared to the healthy controls, GGE patients had significantly less negative gradient scores in the frontoparietal network in the secondary gradient of the beta frequency band (p = 0.0028, after FDR correction). Although no significant difference was observed in the theta frequency band between two groups, the secondary gradient scores in multiple networks (including the visual, somatosensory, dorsal attention and salience) were closely associated with epilepsy duration while controlling for age (Spearman's Rho = 0.5396, -0.6473, -0.6266, -0.75, 0.65, respectively, and all p < 0.05 after FDR correction). The findings were robust against the processing choice on the thresholds, and not accounted for by confounders such as the amount of explained variance by the gradients, or the overall magnitude of the similarity metrics. No significant correlation was found between the gradient scores and the drug load, potentially suggesting a small effect due to the drug treatment.
Conclusions:
The FPN moved closer to the other networks in the patients with GGE and this re-arrangement of the networks might promote the widespread pattern of pathological activity. The alteration that occurred in the FPN for the beta frequency band might help to explain the impairment in the executive function suffered by GGE patients (Ratcliffe et al., 2020). The close association between the gradient scores and epilepsy duration, may suggest a progressive alteration of the network hierarchy along the development and progression of epilepsy. Given that this relationship involves distributed networks in the theta frequency band, it suggests a close interaction between these cortical networks and pathways involved in the generation of theta oscillation, concordant with the thalamo-cortical network disorder postulated in GGE (Bernhardt et al., 2009).
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
EEG/MEG Modeling and Analysis
Novel Imaging Acquisition Methods:
EEG
Keywords:
Electroencephaolography (EEG)
Epilepsy
Other - functional gradient, networks
1|2Indicates the priority used for review
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