Brain structural connectivity sub typing in unilateral temporal lobe epilepsy

The relationship between the interictal epileptiform discharge source connectivity and cortical structural couplings in temporal lobe epilepsy

Objective

The objective of this study was to explore the relation between interictal epileptiform discharge (IED) source connectivity and cortical structural couplings (SCs) in temporal lobe epilepsy (TLE).

Methods

High-resolution 3D-MRI and 32-sensor EEG data from 59 patients with TLE were collected. Principal component analysis was performed on the morphological data on MRI to obtain the cortical SCs. IEDs were labeled from EEG data and averaged. The standard low-resolution electromagnetic tomography analysis was performed to locate the source of the average IEDs. Phase-locked value was used to evaluate the IED source connectivity. Finally, correlation analysis was used to compare the IED source connectivity and the cortical SCs.

Results

The features of the cortical morphology in left and right TLE were similar across four cortical SCs, which could be mainly described as the default mode network, limbic regions, connections bilateral medial temporal, and connections through the ipsilateral insula. The IED source connectivity at the regions of interest was negatively correlated with the corresponding cortical SCs.

Significance

The cortical SCs were confirmed to be negatively related to IED source connectivity in patients with TLE as detected with MRI and EEG coregistered data. These findings suggest the important role of intervening IEDs in treating TLE.

Classification of partial seizures based on functional connectivity: A MEG study with support vector machine

Temporal lobe epilepsy (TLE) is a chronic neurological disorder that is divided into two subtypes, complex partial seizures (CPS) and simple partial seizures (SPS), based on clinical phenotypes. Revealing differences among the functional networks of different types of TLE can lead to a better understanding of the symbology of epilepsy. Whereas Although most studies had focused on differences between epileptic patients and healthy controls, the neural mechanisms behind the differences in clinical representations of CPS and SPS were unclear. In the context of the era of precision, medicine makes precise classification of CPS and SPS, which is crucial. To address the above issues, we aimed to investigate the functional network differences between CPS and SPS by constructing support vector machine (SVM) models. They mainly include magnetoencephalography (MEG) data acquisition and processing, construction of functional connectivity matrix of the brain network, and the use of SVM to identify differences in the resting state functional connectivity (RSFC). The obtained results showed that classification was effective and accuracy could be up to 82.69% (training) and 81.37% (test). The differences in functional connectivity between CPS and SPS were smaller in temporal and insula. The differences between the two groups were concentrated in the parietal, occipital, frontal, and limbic systems. Loss of consciousness and behavioral disturbances in patients with CPS might be caused by abnormal functional connectivity in extratemporal regions produced by post-epileptic discharges. This study not only contributed to the understanding of the cognitive-behavioral comorbidity of epilepsy but also improved the accuracy of epilepsy classification.