Connectivity within regions characterizes epilepsy duration and treatment outcome

Chen, Xue; Wang, Yanjiang; Kopetzky, Sebastian J; Butz‐Ostendorf, Markus; Kaiser, Marcus

Abstract

Finding clear connectome biomarkers for temporal lobe epilepsy (TLE) patients, in particular at early disease stages, remains a challenge. Currently, the whole-brain structural connectomes are analyzed based on coarse parcellations (up to 1,000 nodes). However, such global parcellation-based connectomes may be unsuitable for detecting more localized changes in patients. Here, we use a high-resolution network (~50,000-nodes overall) to identify changes at the local level (within brain regions) and test its relation with duration and surgical outcome. Patients with TLE (n = 33) and age-, sex-matched healthy subjects (n = 36) underwent high-resolution (~50,000 nodes) structural network construction based on deterministic tracking of diffusion tensor imaging. Nodes were allocated to 68 cortical regions according to the Desikan–Killany atlas. The connectivity within regions was then used to predict surgical outcome. MRI processing, network reconstruction, and visualization of network changes were integrated into the NICARA (https://nicara.eu). Lower clustering coefficient and higher edge density were found for local connectivity within regions in patients, but were absent for the global network between regions (68 cortical regions). Local connectivity changes, in terms of the number of changed regions and the magnitude of changes, increased with disease duration. Local connectivity yielded a better surgical outcome prediction (Mean value: 95.39% accuracy, 92.76% sensitivity, and 100% specificity) than global connectivity. Connectivity within regions, compared to structural connectivity between brain regions, can be a more efficient biomarker for epilepsy assessment and surgery outcome prediction of medically intractable TLE.

Citation

Chen, X., Wang, Y., Kopetzky, S. J., Butz‐Ostendorf, M., & Kaiser, M. (2021). Connectivity within regions characterizes epilepsy duration and treatment outcome. Human Brain Mapping, 42(12), 3777-3791. https://doi.org/10.1002/hbm.25464

• Journal Article Type: Article
• Acceptance Date: Apr 26, 2021
• Online Publication Date: May 11, 2021
• Publication Date: Aug 15, 2021
• Deposit Date: Oct 23, 2022
• Publicly Available Date: Oct 24, 2022
• Journal: Human Brain Mapping
• Print ISSN: 1065-9471
• Electronic ISSN: 1097-0193
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 42
• Issue: 12
• Pages: 3777-3791
• DOI: https://doi.org/10.1002/hbm.25464
• Keywords: Neurology (clinical), Neurology, Radiology, Nuclear Medicine and imaging, Radiological and Ultrasound Technology, Anatomy
• Public URL: https://nottingham-repository.worktribe.com/output/9085230
• Publisher URL: https://onlinelibrary.wiley.com/doi/10.1002/hbm.25464