In the vast realm of epilepsy, there exists a fascinating world filled with complex partial seizures (CPS) and simple partial seizures (SPS). These two subtypes, distinguished by their clinical characteristics, have puzzled researchers seeking to comprehend their neural distinctions. While previous studies have primarily compared epileptic patients to healthy individuals, we ventured into uncharted territory. Armed with magnetoencephalography (MEG), we set out to explore the functional network disparities between CPS and SPS. Through the utilization of support vector machine (SVM) models, we unearthed a trove of insights. Our findings demonstrated an impressive classification accuracy of up to 82.69% during training and 81.37% during testing, validating the effectiveness of our approach. Delving deeper into the neurobiology, we discovered that the dissimilarities in functional connectivity were more pronounced in extratemporal regions, specifically situated in the parietal, occipital, frontal, and limbic systems. These disparities shed light on the potential origin of loss of consciousness and behavioral disturbances observed in CPS patients. We invite you to delve into our research and embrace the ever-expanding frontier of epilepsy classification!

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.

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