Translating The Individualized Functional Connectome To Surgical Planning

将个性化功能连接组转化为手术计划

• 批准号: 9052847
• 负责人: Hesheng Liu
• 金额: $ 44.77万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9052847
• 关键词: Address; Algorithms; Anatomy; Area; Atlases; Brain; Characteristics; Clinical; Development; Functional Magnetic Resonance Imaging; Goals; Handedness; Health; Human; Image; Imaging technology; Individual; Laboratories; Language; Language Disorders; Learning; Maps; Methods; Neurologic; Neurosurgical Procedures; Noise; Operative Surgical Procedures; Patients; Population; Postoperative Period; Reproducibility; Research; Rest; Risk; Series; Signal Transduction; Technology; Testing; Translating; United States National Institutes of Health; Validation; base; clinical application; cognitive function; cohort; connectome; design; improved; individualized medicine; innovation; novel strategies; response; tool; treatment planning

 DESCRIPTION (provided by applicant): Localization of brain function is important to minimize functional deficits after neurosurgical procedures. A long-standing goal has been to obtain this information pre-operatively to better predict risk and plan the surgical approach. Although many non-invasive tools are available, fMRI has seen the greatest clinical use. Unfortunately, pre-operative mapping with fMRI suffers from poor signal to noise ratio (SNR) and test-retest reliability at the single-subject level, and the resulting maps are not always consistent with the findings of invasive electrical cortical stimulation (ECS), causing many to question its clinical utility. Recently, our laboratory and others have made major advances that may help address these limitations. Many of these advances have focused on connectivity imaging based on spontaneous activity, catalyzed in part by the NIH Human Connectome Project (HCP). These technical innovations and theoretical advancements are now ripe for being translated to individual clinical patients, but require optimization and validation. The goal of this project is o translate cutting-edge connectivity-based imaging technology to the clinical arena by developing and validating a set of functional mapping tools that can provide individual-level precision and guide surgical intervention. Specifically, we will develop and validate a connectivity-based parcellation technology that can localize functional networks in individual subjects, including in patients with altered brain anatomy. Second, we will develop and validate a connectivity-based method to quantify the lateralization of important cognitive functions and overcome the influence of anatomical asymmetry. Finally, we propose a strategy to improve mapping accuracy when patients are able to perform tasks by flexibly combining the information obtained from spontaneous connectivity and task-evoked responses. This strategy will allow us to leverage the lessons learned from 20 years of exploration using task fMRI and recent revolutionary advancements in connectivity research. The successful completion of this project will greatly improve the clinical value of fMRI in surgical planning, as well as in a wide range of clinical applications. The project will offer a set of comprehensive and extensively tested functional mapping tools suitable for the study of individual subjects with greater sensitivity and reliabilit than are currently available. This increase in mapping precision will directly translate into an enhanced ability to a) predict and reduce postoperative functional deficits, as well as to b) design individualized treatment plans for many neurological and psychiatric patients.

 描述（通过应用程序提供）：大脑功能的定位对于在神经外科手术后最小化功能缺陷很重要。一个长期的目标是在术前获得此信息，以更好地预测风险并计划手术方法。尽管有许多非侵入性工具可用，但fMRI已经看到了最大的临床用途。不幸的是，使用fMRI的术前映射遭受较差的信号与噪声比（SNR）（SNR）和在单个受试者水平上的测试可靠性，以及由此产生的地图并不总是与侵入性电气皮层刺激（EC）的发现一致，从而导致许多人质疑其临床实用性。最近，我们的实验室和其他人取得了重大进展，可以帮助解决这些局限性。这些进步中的许多进步都集中在基于赞助活动的连通性成像上，部分由NIH人类连接项目（HCP）催化。这些技术创新和理论进步现在已经成熟，可以转化为个别临床患者，但需要优化和验证。该项目的目的是通过开发和验证一组功能映射工具，将基于尖端连接的成像技术转换为临床领域，这些功能映射工具可以提供个人级别的精度和指导手术干预。具体而言，我们将开发并验证一种基于连通性的分割技术，该技术可以在各个受试者中定位功能网络，包括在大脑解剖结构改变的患者中。其次，我们将开发并验证一种基于连通性的方法来量化重要的认知功能的横向化并克服解剖学不对称的影响。最后，当患者能够通过灵活地组合从赞助连通性和任务引起的响应中获得的信息来执行任务时，我们提出了一种提高映射准确性的策略。该策略将使我们能够利用fMRI和最近的连通性研究革命进步从20年的探索中学到的经验教训。该项目的成功完成将大大提高fMRI在手术计划中以及广泛的临床应用中的临床价值。该项目将提供一组全面测试的功能映射工具，适用于与目前可用的更高敏感性和可靠性研究的单个受试者。映射精度的这种增加将直接转化为增强的能力，以a）预测和减少潜在的功能性缺陷，以及b）为许多神经系统和精神病患者设计个性化的治疗计划。