Details

Autor(en) / Beteiligte

• Bauer, Tobias
• David, Bastian
• Ernst, Leon
• Becker, Albert J.
• Witt, Juri‐Alexander
• Helmstaedter, Christoph
• Wagner, Jan
• Weber, Bernd
• Elger, Christian E.
• Surges, Rainer
• Rüber, Theodor

Titel

Structural network topology in limbic encephalitis is associated with amygdala enlargement, memory performance and serostatus

Ist Teil von

• Epilepsia (Copenhagen), 2020-10, Vol.61 (10), p.e140-e145

Ort / Verlag

United States: Wiley Subscription Services, Inc

Erscheinungsjahr

2020

Quelle

Electronic Journals Library

Beschreibungen/Notizen

• Limbic encephalitis (LE) forms a spectrum of autoimmune diseases involving temporal lobe epilepsy and memory impairment. Imaging features of LE are known to depend on the associated antibody and to occur on the brain network level. However, first studies investigating brain networks in LE have either focused on one distinct antibody subgroup or on distinct anatomical regions. In this study, brain graphs of 17 LE patients with autoantibodies against glutamic acid decarboxylase 65 (GAD‐LE), four LE patients with autoantibodies against leucine‐rich glioma‐inactivated 1, five LE patients with autoantibodies against contactin‐associated protein‐like 2, 26 age‐ and gender‐matched healthy control subjects, and 20 epilepsy control patients with hippocampal sclerosis were constructed based on T1‐weighted structural magnetic resonance imaging scans and diffusion tensor imaging. GAD‐LE showed significantly altered global network topology in terms of integration and segregation as compared to healthy controls and patients with hippocampal sclerosis (P < .01, analysis of variance with Tukey‐Kramer post hoc tests). Linear regression linked global network measures with amygdala volume and verbal memory performance (P < .05). Alterations of local network topology show serotype dependence in hippocampus, amygdala, insula, and various cortical regions. Our findings reveal serotype‐dependent patterns of structural connectivity and prove the relevance of in silico network measures on clinical grounds.