脑网络组研究知识库

Brain arteriovenous malformation is a complex cerebrovascular disease characterized by the lack of capillaries between the cerebral arteries and cerebral veins at the lesion site, resulting in the formation of malformed vascular masses. For patients with unruptured brain arteriovenous malformation, the proportion of seizures is 24-40%, which is the most common clinical manifestation. However, the mechanism of seizures in patients with brain arteriovenous malformation is still unclear, and it is difficult to effectively predict the risk of seizures in patients with brain arteriovenous malformation. Therefore, based on the structural magnetic resonance imaging data of 197 patients with unruptured brain arteriovenous malformation collected by Tiantan Hospital, and the resting-state functional magnetic resonance imaging data of 356 normal people collected in the early stage of the research group, , this paper used brain network and machine learning technology to explore the neural mechanism behind epileptic seizures in patients with unruptured brain arteriovenous malformation, and established a risk prediction model for epilepsy, which provided new findings for the study of epilepsy risk of brain arteriovenous malformation.

This paper hypothesized that seizures in patients with unruptured brain arteriovenous malformations are not related to the location of the lesion, but rather to changes in functional connectivity. This paper first used a voxel-based lesion mapping technique to test this hypothesis and found that lesions in patients with unruptured brain arteriovenous malformations are heterogeneous in location, with no single region thought to be anatomically associated with seizures. Further through brain network techniques, we found that the lesions of  seizure patients map to a common functional network defined by negative connections to the precuneus and positive connections to the left caudate nucleus and anterior cerebellum, and that this connectivity pattern is specific for brain arteriovenous malformation-related seizures. Based on the obtained brain network region, the seizure risk of patients with brain arteriovenous malformation can be predicted and verified in an independent validation set. By overlaying analysis with seven typical resting state networks, we found that frontoparietal control network, limbic network and default mode network are epileptogenic networks in patients with unruptured brain arteriovenous malformations.

In order to further verify the previous hypothesis, this paper used the structural magnetic resonance imaging  data of patients with unruptured brain arteriovenous malformation and the functional connectivity data calculated based on normal persons as features, using machine learning algorithms to classify seizures in patients with unruptured brain arteriovenous malformations. This paper found that, for patients with unruptured brain arteriovenous malformations, the classification performance using structural image data was significantly lower than that using functional connectivity data, both in the training set and in the testing set.This further indicated that although the locations of different lesions are in different brain regions, they belong to a common brain network. Locally lesion triggers changes in global brain network function that induce epilepsy.The best machine learning model established in this paper has a classification AUC of 80%, and its accuracy in the training set and testing set is 75.46% and 67.65%, respectively, indicating that the model has better performance for unruptured cerebral arteriovenous malformation seizures, which will aid in the precise diagnosis of seizures in patients with unruptured brain arteriovenous malformations.This paper also analyzed the interpretability of machine learning models, and we found that the brain regions affected by the brain network-based method are consistent with the important brain regions discovered by the machine learning-based method, which provided insights into the neural mechanism of epileptic seizures caused by unruptured brain arteriovenous malformations.

In conclusion, this paper conducted a multifaceted study on the risk of epilepsy in patients with unruptured brain arteriovenous malformation, found the epilepsy network associated with unruptured brain arteriovenous malformation, and established a high accuracy prediction model for unruptured brain arteriovenous malformation seizures. This paper also explained the neural mechanism behind epileptic seizures in patients with brain arteriovenous malformations, which provided clues and basis for understanding the disease mechanism. This paper has important scientific significance and potential clinical value.