Predicting Epilepsy Surgery Outcomes Using Neural Network Architecture

使用神经网络架构预测癫痫手术结果

• 批准号: 10619937
• 负责人: Leonardo F Bonilha
• 金额: $ 82.07万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10619937
• 关键词: Ablation; Anterior Temporal Lobectomy; Area; Basic Science; Brain; Brain Mapping; Caring; Clinical; Clinical Data; Common Data Element; Contralateral; Counseling; Data; Data Collection; Detection; Diffusion; Diffusion Magnetic Resonance Imaging; Electrocoagulation; Electroencephalography; Ensure; Epilepsy; Evaluation; Excision; Failure; Fiber; Freedom; Functional Magnetic Resonance Imaging; Functional disorder; Future; Hippocampus (Brain); Image; Individual; Intractable Epilepsy; Knowledge; Lesion; Location; Machine Learning; Magnetic Resonance Imaging; Measures; Medial; Methods; Modality; Modeling; Modernization; Neurobiology; Neurons; Operative Surgical Procedures; Outcome; Pathologic; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacology; Phenotype; Predictive Value; Procedures; Prospective Studies; Prospective cohort; Publishing; Refractory; Reproducibility; Research; Resistance; Rest; Risk; Scalp structure; Seizures; Sensitivity and Specificity; Severities; Structure; Temporal Lobe Epilepsy; Testing; Thalamic structure; Tonic-Clonic Epilepsy; Treatment outcome; United States National Institutes of Health; Validation; base; computerized tools; connectome; experience; gray matter; hippocampal atrophy; imaging modality; improved; millimeter; multimodality; neural network; neural network architecture; neuroimaging; neuronal circuitry; novel strategies; outcome prediction; predictive modeling; prospective; recruit; response; standard of care; success; surgery outcome; tractography; white matter

Abstract Temporal lobe epilepsy (TLE) is one of the most common forms of pharmacologically resistant epilepsy. The resection or ablation of medial temporal structures can be curative for many patients. Unfortunately, approximately one third of patients who undergo TLE surgery continue to have disabling seizures post- procedurally. The reasons for suboptimal outcomes are not well understood and therefore constitute a very important knowledge gap in epilepsy care. A better understanding of this difference in surgical response phenotype could be used to improve surgical planning, treatment, outcome prediction and counseling. Promising preliminary studies suggest that TLE surgical outcomes can be inferred by neuroimaging computational tools assessing the cumulative degree of abnormalities in the topological organization of structural networks involving limbic and extra-limbic regions. Nonetheless, network abnormalities are not routinely or systematically used and quantified in the pre-surgical evaluation of epilepsies, and their assessment requires refinement and further validation. The purpose of this proposal is to perform a prospective study to test the hypothesis that the degree of limbic and extra-limbic network abnormalities in TLE, systematically assessed using a connectome approach based on optimized diffusion MRI (dMRI), can be used to predict and better understand epilepsy surgery outcomes. This hypothesis builds on the well-defined basic science and neurobiological premises that epilepsy is associated with pathological alterations in networks that are related to seizure onset and seizure propagation. Importantly, network abnormalities are not visible on routine MRI, but their detection using connectomes constitutes a modern approach to quantifying the location and magnitude of “lesional epilepsy,” where broad computational network abnormalities imply worse outcomes. We will prospectively gather clinical and imaging data at six epilepsy centers using the NIH epilepsy common data elements. This project will be fundamentally based on standard of care data, thus minimizing the burden of extra data collection and ensuring feasibility. Furthermore, this project will be embedded in the ENIGMA-Epilepsy framework, which is a collaborative platform for clinical and neuromaging multi-center research. Specific Aim 1 will define the accuracy, reproducibility, and predictive values of the pre-surgical dMRI tractography connectome model towards surgical results in TLE. We will perform hypothesis-driven tests of specific limbic and extra-limbic networks in relationship with clinical data and surgical outcomes. Specific Aim 2 will test if the neuroimaging-clinical outcome model can be further improved with advanced diffusion methods (multi-shell diffusional kurtosis imaging), resting state functional MRI networks, or a multimodal approach. We believe that this research will have an important impact on our understanding of the mechanisms related to TLE treatment.

摘要