Siqi Yang1,2, Zhiqiang Zhang3, Huafu Chen1,2, Yao Meng1,2, Jiao Li1,2, Zehan Li1,2, Qiang Xu3, Qirui Zhang1,2, Yun-Shuang Fan1,2, Guangming Lu3, Wei Liao1,2. 1. The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China. 2. MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China. 3. Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
Abstract
OBJECTIVE: Epilepsies are a group of neurological disorders sharing certain core features, but also demonstrate remarkable pathogenic and symptomatic heterogeneities. Various subtypes of epilepsy have been identified with abnormal shift in the brain default mode network (DMN). This study aims to evaluate the fine details of shared and distinct alterations in the DMN among epileptic subtypes. METHODS: We collected resting-state functional magnetic resonance imaging (MRI) data from a large epilepsy sample (n = 371) at a single center, including temporal lobe epilepsy (TLE), frontal lobe epilepsy (FLE), and genetic generalized epilepsy with generalized tonic-clonic seizures (GGE-GTCS), as well as healthy controls (HC, n = 150). We analyzed temporal dynamics profiling of the DMN, including edge-wise and node-wise temporal variabilities, and recurrent dynamic states of functional connectivity, to identify abnormalities common to epilepsies as well as those specific to each subtype. RESULTS: The analyses revealed that hypervariable edges within the specific DMN subsystem were shared by all subtypes (all PNBS < .005), and deficits in node-wise temporal variability were prominent in TLE (all t(243) ≤ 2.51, PFDR < .05) and FLE (all t(302) ≤ -2.65, PFDR < .05) but relatively weak in GGE-GTCS. Moreover, dynamic states were generally less stable in patients than controls (all P's < .001). SIGNIFICANCE: Collectively, these findings demonstrated general DMN abnormalities common to different epilepsies as well as distinct dysfunctions to subtypes, and provided insights into understanding the relationship of pathophysiological mechanisms and brain connectivity.
OBJECTIVE:Epilepsies are a group of neurological disorders sharing certain core features, but also demonstrate remarkable pathogenic and symptomatic heterogeneities. Various subtypes of epilepsy have been identified with abnormal shift in the brain default mode network (DMN). This study aims to evaluate the fine details of shared and distinct alterations in the DMN among epileptic subtypes. METHODS: We collected resting-state functional magnetic resonance imaging (MRI) data from a large epilepsy sample (n = 371) at a single center, including temporal lobe epilepsy (TLE), frontal lobe epilepsy (FLE), and genetic generalized epilepsy with generalized tonic-clonic seizures (GGE-GTCS), as well as healthy controls (HC, n = 150). We analyzed temporal dynamics profiling of the DMN, including edge-wise and node-wise temporal variabilities, and recurrent dynamic states of functional connectivity, to identify abnormalities common to epilepsies as well as those specific to each subtype. RESULTS: The analyses revealed that hypervariable edges within the specific DMN subsystem were shared by all subtypes (all PNBS < .005), and deficits in node-wise temporal variability were prominent in TLE (all t(243) ≤ 2.51, PFDR < .05) and FLE (all t(302) ≤ -2.65, PFDR < .05) but relatively weak in GGE-GTCS. Moreover, dynamic states were generally less stable in patients than controls (all P's < .001). SIGNIFICANCE: Collectively, these findings demonstrated general DMN abnormalities common to different epilepsies as well as distinct dysfunctions to subtypes, and provided insights into understanding the relationship of pathophysiological mechanisms and brain connectivity.
Authors: Taha Gholipour; Xiaozhen You; Steven M Stufflebeam; Murray Loew; Mohamad Z Koubeissi; Victoria L Morgan; William D Gaillard Journal: Epilepsia Date: 2022-01-04 Impact factor: 6.740
Authors: Katrin M Beckmann; Adriano Wang-Leandro; Henning Richter; Rima N Bektas; Frank Steffen; Matthias Dennler; Ines Carrera; Sven Haller Journal: Sci Rep Date: 2021-12-13 Impact factor: 4.379
Authors: Aaron F Struck; Melanie Boly; Gyujoon Hwang; Veena Nair; Jedidiah Mathis; Andrew Nencka; Lisa L Conant; Edgar A DeYoe; Manoj Raghavan; Vivek Prabhakaran; Jeffrey R Binder; Mary E Meyerand; Bruce P Hermann Journal: Epilepsy Behav Date: 2021-02-18 Impact factor: 2.937