Establishing an Atrophy-Based Functional Network Model as a Biomarker for Seizure-Onset Laterality

建立基于萎缩的功能网络模型作为癫痫发作偏侧性的生物标志物

• 批准号: 10751261
• 负责人: Jonathan Michael Towne
• 金额: $ 3.69万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCIENCE CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-12-01 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10751261
• 关键词: Address; Amnesia; Animal Model; Anterograde Amnesia; Atlases; Atrophic; Bilateral; Biological Markers; Brain region; Caring; Classification; Clinical; Data; Data Analyses; Disease; Electroencephalography; Epilepsy; Equation; Excision; Exhibits; Fellowship; Functional Magnetic Resonance Imaging; Future; Goals; Handedness; Health; Human; Individual; Left; Literature; Logistic Regressions; Magnetic Resonance Imaging; Medial; Medial Dorsal Nucleus; Meta-Analysis; Methodology; Methods; Modeling; Multiple Sclerosis; Network-based; Operative Surgical Procedures; Pathologic; Pathology; Patients; Physiology; Predisposition; Property; Publishing; Reporting; Reproducibility; Research; Research Personnel; Resort; Rest; Risk; Seizures; Specificity; Techniques; Temporal Lobe; Temporal Lobe Epilepsy; Thalamic structure; Tissues; Training; Validation; Visual; Weight; With laterality; biomarker validation; cerebral atrophy; clinical biomarkers; detection method; diagnostic biomarker; experimental study; graph theory; imaging biomarker; improved; innovation; network models; neuroimaging; neurosurgery; non-invasive imaging; noninvasive diagnosis; predictive modeling; regional atrophy; skills; statistics; success

Abstract . MTLE is the most common type of epilepsy referred for surgery. Most MTLE seizures begin unilaterally and respond well to resection of the epileptogenic tissue. Surgical success, however, is predicated on correct identification of seizure-onset laterality. Non-invasive methods for determining onset laterality are seldom definitive since MTLE seizures rapidly spread beyond the epileptogenic zone to extratemporal regions. Functional network modeling is a promising method for establishing non-invasive biomarkers; in MTLE, this method detects modulations in functional connectivity (edges) between pathologic regions (nodes) involved in extratemporal seizure spread. However, many modeling techniques define nodes by arbitrary atlas-based approaches that introduce bias in assessments of connectivity that is detrimental to model accuracy. An incomplete understanding of MTLE connectivity (particularly with extratemporal regions of pathology) has contributed to why non-invasive imaging biomarkers have yet to be approved for use in individual MTLE patients. One robust approach for developing functional network models uses meta-analysis to define nodes and then computes functional connectivity between those data-driven regions. This approach has been successfully used to construct a clinically validated biomarker for network disruption in multiple sclerosis. In 2013, meta-analytic modeling was first applied to MTLE. First, atrophy was identified in the medial temporal lobe and thalamic medial dorsal nucleus (MDN). Then, a model of the functional connectivity between these atrophic regions was constructed and used to predict seizure-onset laterality (86% sensitivity, 100% specificity). Until recently, insufficient literature limited this meta-analytic model’s accuracy and clinical utility. However, the body of MTLE literature has grown considerably (over double), presenting an exciting opportunity to expand this model and, ideally, improve prediction accuracy of seizure-onset laterality to clinically useful levels. The proposed strategy will address the hypothesis that: MTLE is a network-based disorder exhibiting lateralized changes in function at rest; these changes will predict seizure-onset laterality with >97% sensitivity without loss of specificity. In this proposal, MTLE connectivity will be studied in three specific aims by constructing 1) meta-analytic models of health and disease connectivity, 2) group-wise models of connectivity in MTLE patients and healthy controls using primary resting-state fMRI data, and 3) per-subject models of left- and right- lateralized MTLE patients using rs-fMRI. Crucially, all models will be data-driven using nodes defined by meta-analysis. Using both sparse and rich modeling approaches for each level of analysis (each aim), the proposed studies will identify paths of MTLE seizure propagation (Aim 1), quantify the effects of MTLE (versus healthy controls) on network connectivity (Aim 2), and validate a non-invasive diagnostic biomarker to predict seizure-onset laterality, per-subject, in MTLE (Aim 3). Completion of these aims will further establish a pipeline for developing noninvasive clinical biomarkers for other epilepsies while providing critical training for a future independent neurosurgical investigator.

摘要。