Multimodal Imaging of Language in the Preoperative Evaluation of Epilepsy

描述（由申请人提供）：拟议的研究将调查使用非侵入性多模态成像来评估颞叶癫痫（TLE）患者的术前语言处理和预测术后语言结果。这项研究将通过结合脑磁图（MEG），功能磁共振成像（fMRI），扩散张量成像（DTI）与高分辨率结构MRI来完成。研究目标是阐明（1）哪些大脑区域参与语言处理，以及（2）是否结构性测量（即，皮质厚度和白色物质纤维束成像）可以与MEG和fMRI激活模式相结合，以优化TLE术后语言结果的预测，这是一个具有重要和直接临床意义的问题。这些目标将通过以下方法实现。首先，时间过程和空间范围的神经激活将比较TLE患者和健康对照组的词汇语义处理任务期间，使用MEG和功能磁共振成像告知从结构MRI的皮质重建。其次，将使用DTI量化关键白色物质纤维束的完整性。第三，皮质厚度和海马体积将从结构MRI中量化。第四，功能（MEG/ fMRI）和结构（MRI/DTI）数据将结合预测TLE术后语言能力下降的风险。本研究具有很大的临床实用性，因为识别TLE语言处理的结构和功能相关性可以帮助术前计划，并可能降低与切除功能皮层相关的发病率。该提案的目标与NINDS癫痫研究基准（第IIIB部分，确定有助于合并症的预测因子和潜在机制）一致，该标准鼓励结合使用创新的结构和功能神经影像学方法，以确定有助于癫痫认知和行为合并症的预测因子和潜在机制。癫痫是一种常见的神经系统疾病，每年花费医疗保健系统约125亿美元，并可能对个人的生活质量，就业和健康状况产生负面影响。目前的项目对公共卫生有很大的影响，因为它致力于通过使用先进的非侵入性技术来识别癫痫患者脑功能障碍的区域，从而改善癫痫患者的术前评估。 公共卫生相关性：该项目将研究如何使用非侵入性神经影像学来研究癫痫患者的术前语言表现，并确定术后语言下降的风险患者。这项研究将通过结合脑磁图，功能性磁共振成像，扩散张量成像与高分辨率结构MRI来完成。从这项调查中获得的信息可能有助于手术计划，并减少癫痫患者术后语言能力下降的可能性。

DESCRIPTION (provided by applicant): The proposed research will investigate the use of non-invasive multimodal imaging to evaluate preoperative language processing and predict postsurgical language outcome in patients with temporal lobe epilepsy (TLE). This research will be accomplished by combining magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI) with high-resolution structural MRI. The research goals are to elucidate (1) what brain regions are involved in language processing, and (2) whether or not structural measures (i.e., cortical thickness and white matter tractography) can be combined with MEG and fMRI activation patterns to optimize the prediction of postoperative language outcome in TLE, an issue with important and immediate clinical implications. These goals will be accomplished using the following methods. First, the time course and spatial extent of neural activations will be compared in patients with TLE and healthy controls during tasks of lexical-semantic processing using MEG and fMRI informed by cortical reconstruction from structural MRI. Second, the integrity of key white matter fiber tracts will be quantified using DTI. Third, cortical thickness and hippocampal volumes will be quantified from structural MRI. Fourth, functional (MEG/ fMRI) and structural (MRI/DTI) data will be combined to predict the risk for postoperative language decline in TLE. This study has great clinical utility in that identifying the structural and functional correlates of language processing in TLE can assist with preoperative planning and possibly reduce morbidity associated with removal of eloquent cortex. The goals of this proposal are aligned with the NINDS Benchmarks for Epilepsy Research (Part IIIB, Identify predictors and underlying mechanisms that contribute to co-morbidities), which encourage the combined use of innovative structural and functional neuroimaging methods for identifying the predictors and underlying mechanisms that contribute to cognitive and behavioral co-morbidities in epilepsy. Epilepsy is a common neurological disease that costs the healthcare system approximately $12.5 billion annually and can negatively impact an individual's quality of life, employment, and health status. The current project has strong implications for public health because it strives to improve the presurgical evaluation of patients with epilepsy by using advanced, noninvasive technology to identify regions of brain dysfunction in epilepsy. PUBLIC HEALTH RELEVANCE: This project will investigate how non-invasive neuroimaging can be used to study preoperative language performances in patients with epilepsy, and identify patients at risk for postoperative language decline. This research will be accomplished by combining magnetoencephalography, functional magnetic resonance imaging, and diffusion tensor imaging with high-resolution structural MRI. The information gained from this investigation may facilitate surgical planning and reduce the potential for postoperative language decline in patients with epilepsy.