Microstate-based brain network dynamics distinguishing temporal lobe epilepsy patients: A machine learning approach

• Published in: NeuroImage (Orlando, Fla.) Vol. 296; p. 120683
• Main Authors: Wei, Zihan, Wang, Xinpei, Liu, Chao, Feng, Yan, Gan, Yajing, Shi, Yuqing, Wang, Xiaoli, Liu, Yonghong, Deng, Yanchun
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-08-2024; Elsevier Limited; Elsevier
• Subjects: Adult; Brain - diagnostic imaging; Brain - physiopathology; Brain research; Connectome - methods; Convulsions & seizures; Drug resistance; Drug Resistant Epilepsy - diagnostic imaging; Drug Resistant Epilepsy - physiopathology; Electrodes; Electroencephalography; Epilepsy; Epilepsy, Temporal Lobe - diagnostic imaging; Epilepsy, Temporal Lobe - physiopathology; Female; Functional connectivity; Humans; Learning algorithms; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Microstate; Middle Aged; Nerve Net - diagnostic imaging; Nerve Net - physiopathology; Neural networks; Patients; Seizures; Software; Spatiotemporal variability; Synchronization; Temporal lobe; Temporal lobe epilepsy; Young Adult; Functional connectivity; Temporal lobe epilepsy; Microstate; Spatiotemporal variability; Machine learning
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2024.120683

•Even without IEDs, there is a generalized acceleration of brain activity in TLE patients.•Brain network in TLE patients were highly synchronized yet highly unstable, which might be related to epileptogensis.•Highly independent, highly stable, and highly synchronized activity in bilateral frontal regions and left temporal region, might be related to drug resistance in TLE.•Machine learning approach using spatiotemporal metrics can accurately distinguish TLE patients from HC and DRE patients from DSE.
Temporal lobe epilepsy (TLE) stands as the predominant adult focal epilepsy syndrome, characterized by dysfunctional intrinsic brain dynamics. However, the precise mechanisms underlying seizures in these patients remain elusive. Our study encompassed 116 TLE patients compared with 51 healthy controls. Employing microstate analysis, we assessed brain dynamic disparities between TLE patients and healthy controls, as well as between drug-resistant epilepsy (DRE) and drug-sensitive epilepsy (DSE) patients. We constructed dynamic functional connectivity networks based on microstates and quantified their spatial and temporal variability. Utilizing these brain network features, we developed machine learning models to discriminate between TLE patients and healthy controls, and between DRE and DSE patients. Temporal dynamics in TLE patients exhibited significant acceleration compared to healthy controls, along with heightened synchronization and instability in brain networks. Moreover, DRE patients displayed notably lower spatial variability in certain parts of microstate B, E and F dynamic functional connectivity networks, while temporal variability in certain parts of microstate E and G dynamic functional connectivity networks was markedly higher in DRE patients compared to DSE patients. The machine learning model based on these spatiotemporal metrics effectively differentiated TLE patients from healthy controls and discerned DRE from DSE patients. The accelerated microstate dynamics and disrupted microstate sequences observed in TLE patients mirror highly unstable intrinsic brain dynamics, potentially underlying abnormal discharges. Additionally, the presence of highly synchronized and unstable activities in brain networks of DRE patients signifies the establishment of stable epileptogenic networks, contributing to the poor responsiveness to antiseizure medications. The model based on spatiotemporal metrics demonstrated robust predictive performance, accurately distinguishing both TLE patients from healthy controls and DRE patients from DSE patients.