Dataset Information

Structural Brain Network Abnormalities and the Probability of Seizure Recurrence After Epilepsy Surgery.

ABSTRACT:

Objective

We assessed preoperative structural brain networks and clinical characteristics of patients with drug-resistant temporal lobe epilepsy (TLE) to identify correlates of postsurgical seizure recurrences.

Methods

We examined data from 51 patients with TLE who underwent anterior temporal lobe resection (ATLR) and 29 healthy controls. For each patient, using the preoperative structural, diffusion, and postoperative structural MRI, we generated 2 networks: presurgery network and surgically spared network. Standardizing these networks with respect to controls, we determined the number of abnormal nodes before surgery and expected to be spared by surgery. We incorporated these 2 abnormality measures and 13 commonly acquired clinical data from each patient into a robust machine learning framework to estimate patient-specific chances of seizures persisting after surgery.

Results

Patients with more abnormal nodes had a lower chance of complete seizure freedom at 1 year and, even if seizure-free at 1 year, were more likely to relapse within 5 years. The number of abnormal nodes was greater and their locations more widespread in the surgically spared networks of patients with poor outcome than in patients with good outcome. We achieved an area under the curve of 0.84 ± 0.06 and specificity of 0.89 ± 0.09 in predicting unsuccessful seizure outcomes (International League Against Epilepsy [ILAE] 3-5) as opposed to complete seizure freedom (ILAE 1) at 1 year. Moreover, the model-predicted likelihood of seizure relapse was significantly correlated with the grade of surgical outcome at year 1 and associated with relapses up to 5 years after surgery.

Conclusion

Node abnormality offers a personalized, noninvasive marker that can be combined with clinical data to better estimate the chances of seizure freedom at 1 year and subsequent relapse up to 5 years after ATLR.

Classification of evidence

This study provides Class II evidence that node abnormality predicts postsurgical seizure recurrence.

SUBMITTER: Sinha N

PROVIDER: S-EPMC7884990 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Approximately 50% of individuals who undergo resective epilepsy surgery experience seizure recurrence. The heterogenous post-operative outcomes are not fully explained by clinical, imaging and electrophysiological variables. We hypothesized that molecular features may be useful in understanding surgical response, and that individuals with epilepsy can be classified into molecular subtypes that are associated with seizure freedom or recurrence after surgical resection. Pre-operative blood samples, brain tissue and post-operative seizure outcomes were collected from a cohort of 40 individuals with temporal lobe epilepsy, 23 of whom experienced post-operative seizure recurrence. Messenger RNA and microRNA extracted from the blood and tissue samples were sequenced. The messenger RNA and microRNA expression levels from the blood and brain were each subjected to a novel clustering approach combined with multiple logistic regression to separate individuals into genetic clusters that identify novel subtypes associated with post-operative seizure outcomes. We then compared the microRNAs and messenger RNAs from patient blood and brain tissue that were significantly associated with each subtype to identify signatures that are similarly over- or under-represented for an outcome and more likely to represent endophenotypes with common molecular aetiology. These target microRNAs and messenger RNAs were further characterized by pathway analysis to assess their functional role in epilepsy. Using blood-derived microRNA and messenger RNA expression levels, we identified two subtypes of epilepsy that were significantly associated with seizure recurrence (clusters A1 and B4) (adjusted P < 0.20). A total of 551 microRNAs and 2486 messenger RNAs were associated with clusters A1 and B4, respectively (adjusted P < 0.05). Clustering of brain-tissue messenger RNA expression levels revealed an additional subtype (C2) associated with seizure recurrence that had high overlap of dysregulated messenger RNA transcripts with cluster B4. Clusters A1, B4 and C2 also shared significant overlap of subjects, which altogether suggests a coordinated mechanism by which microRNA and messenger RNA transcripts may be related to seizure recurrence. Epileptic subtypes A1, B4 and C2 reveal both known and novel microRNA and messenger RNA targets in seizure recurrence. Furthermore, targets identified in A1 and B4 are quantifiable in pre-operative blood samples and could potentially serve as biomarkers for surgical resection outcomes.