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Altered correlation of concurrently recorded EEG-fMRI connectomes in temporal lobe epilepsy.

Authors :
Wirsich J
Iannotti GR
Ridley B
Shamshiri EA
Sheybani L
Grouiller F
Bartolomei F
Seeck M
Lazeyras F
Ranjeva JP
Guye M
Vulliemoz S
Source :
Network neuroscience (Cambridge, Mass.) [Netw Neurosci] 2024 Jul 01; Vol. 8 (2), pp. 466-485. Date of Electronic Publication: 2024 Jul 01 (Print Publication: 2024).
Publication Year :
2024

Abstract

Whole-brain functional connectivity networks (connectomes) have been characterized at different scales in humans using EEG and fMRI. Multimodal epileptic networks have also been investigated, but the relationship between EEG and fMRI defined networks on a whole-brain scale is unclear. A unified multimodal connectome description, mapping healthy and pathological networks would close this knowledge gap. Here, we characterize the spatial correlation between the EEG and fMRI connectomes in right and left temporal lobe epilepsy (rTLE/lTLE). From two centers, we acquired resting-state concurrent EEG-fMRI of 35 healthy controls and 34 TLE patients. EEG-fMRI data was projected into the Desikan brain atlas, and functional connectomes from both modalities were correlated. EEG and fMRI connectomes were moderately correlated. This correlation was increased in rTLE when compared to controls for EEG-delta/theta/alpha/beta. Conversely, multimodal correlation in lTLE was decreased in respect to controls for EEG-beta. While the alteration was global in rTLE, in lTLE it was locally linked to the default mode network. The increased multimodal correlation in rTLE and decreased correlation in lTLE suggests a modality-specific lateralized differential reorganization in TLE, which needs to be considered when comparing results from different modalities. Each modality provides distinct information, highlighting the benefit of multimodal assessment in epilepsy.<br />Competing Interests: Competing Interests: The authors have declared that no competing interests exist.<br /> (© 2024 Massachusetts Institute of Technology.)

Details

Language :
English
ISSN :
2472-1751
Volume :
8
Issue :
2
Database :
MEDLINE
Journal :
Network neuroscience (Cambridge, Mass.)
Publication Type :
Academic Journal
Accession number :
38952816
Full Text :
https://doi.org/10.1162/netn_a_00362