Disruptions in modular structure and network integration of language-related network predict language performance in temporal lobe epilepsy: Evidence from graph-based analysis

Victor Karpychev; Svetlana Malyutina; Anna Zhuravleva; Oleg Bronov; Vasiliy Kuzin; Aleksei Marinets; Olga Dragoy

doi:10.1016/j.yebeh.2023.109407

Disruptions in modular structure and network integration of language-related network predict language performance in temporal lobe epilepsy: Evidence from graph-based analysis

Epilepsy Behav. 2023 Oct:147:109407. doi: 10.1016/j.yebeh.2023.109407. Epub 2023 Sep 7.

Authors

Victor Karpychev¹, Svetlana Malyutina², Anna Zhuravleva², Oleg Bronov³, Vasiliy Kuzin³, Aleksei Marinets³, Olga Dragoy⁴

Affiliations

¹ Center for Language and Brain, HSE University, Moscow, Russian Federation. Electronic address: karpuchevvictor@gmail.com.
² Center for Language and Brain, HSE University, Moscow, Russian Federation.
³ National Medical and Surgical Center named after N.I. Pirogov, Moscow, Russian Federation.
⁴ Center for Language and Brain, HSE University, Moscow, Russian Federation; Institute of Linguistics, Russian Academy of Sciences, Moscow, Russian Federation.

PMID: 37688840
DOI: 10.1016/j.yebeh.2023.109407

Abstract

Objective: Temporal lobe epilepsy (TLE) is a network disorder that alters the total organization of the language-related network. Task-based functional magnetic resonance imaging (fMRI) aimed at functional connectivity is a direct method to investigate how the network is reorganized. However, such studies are scarce and represented mostly by the resting-state analysis of the individual connections between regions. To fill this gap, we used a graph-based analysis, which allows us to cover the total language-related network changes, such as disruptions in an integration/segregation balance, during a language task in TLE.

Methods: We collected task-based fMRI data with sentence completion from 19 healthy controls and 28 people with left TLE. Using graph-based analysis, we estimated how the language-related network segregated into modules and tested whether they differed between groups. We evaluated the total network integration and the integration within modules. To assess intermodular integration, we considered the number and location of connector hubs-regions with high connectivity.

Results: The language-related network was differently segregated during language processing in the groups. While healthy controls showed a module consisting of left perisylvian regions, people with TLE exhibited a bilateral module formed by the anterior language-related areas and a module in the left temporal lobe, reflecting hyperconnectivity within the epileptic focus. As a consequence of this reorganization, there was a statistical tendency that the dominance of the intramodular integration over the total network integration was greater in TLE, which predicted language performance. The increase in the number of connector hubs in the right hemisphere, in turn, was compensatory in TLE.

Significance: Our study provides insights into the reorganization of the language-related network in TLE, revealing specific network changes in segregation and integration. It confirms reduced global connectivity and compensation across the healthy hemisphere, commonly observed in epilepsy. These findings advance the understanding of the network-based reorganizational processes underlying language processing in TLE.

Keywords: Graph theory; Language processing; Network reorganization; Task-based functional magnetic resonance imaging (fMRI); Temporal lobe epilepsy (TLE).