Dataset Information

Identification of neural networks preferentially engaged by epileptogenic mass lesions through lesion network mapping analysis.

ABSTRACT: Lesion network mapping (LNM) has been applied to true lesions (e.g., cerebrovascular lesions in stroke) to identify functionally connected brain networks. No previous studies have utilized LNM for analysis of intra-axial mass lesions. Here, we implemented LNM for identification of potentially vulnerable epileptogenic networks in mass lesions causing medically-refractory epilepsy (MRE). Intra-axial brain lesions were manually segmented in patients with MRE seen at our institution (EL_INST). These lesions were then normalized to standard space and used as seeds in a high-resolution normative resting state functional magnetic resonance imaging template. The resulting connectivity maps were first thresholded (p_{Bonferroni_cor}??2.0); canonical resting-state networks preferentially engaged by EL_INSTs were the Limbic and the Frontoparietal Networks (Mean VOR?>?1.5). In this proof of concept study, we demonstrate the feasibility of LNM for intra-axial mass lesions by showing that ELs have discrete functional connections and may preferentially engage in discrete resting-state networks. Thus, the underlying normative neural circuitry may, in part, explain the propensity of particular lesions toward the development of MRE. If prospectively validated, this has ramifications for patient counseling along with both approach and timing of surgery for lesions in locations prone to development of MRE.

SUBMITTER: Mansouri AM

PROVIDER: S-EPMC7335039 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Identification of neural networks preferentially engaged by epileptogenic mass lesions through lesion network mapping analysis.

Mansouri Alireza M AM Germann Jürgen J Boutet Alexandre A Elias Gavin J B GJB Mithani Karim K Chow Clement T CT Karmur Brij B Ibrahim George M GM McAndrews Mary Pat MP Lozano Andres M AM Zadeh Gelareh G Valiante Taufik A TA

Scientific reports 20200703 1

Lesion network mapping (LNM) has been applied to true lesions (e.g., cerebrovascular lesions in stroke) to identify functionally connected brain networks. No previous studies have utilized LNM for analysis of intra-axial mass lesions. Here, we implemented LNM for identification of potentially vulnerable epileptogenic networks in mass lesions causing medically-refractory epilepsy (MRE). Intra-axial brain lesions were manually segmented in patients with MRE seen at our institution (EL_INST). These ...[more]

PMID: 32620922

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Dataset Information

Identification of neural networks preferentially engaged by epileptogenic mass lesions through lesion network mapping analysis.

Publications

Identification of neural networks preferentially engaged by epileptogenic mass lesions through lesion network mapping analysis.

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