A Comprehensive Clinical fMRI Software Solution to Enable Mapping of Critical Functional Networks and Cerebrovascular Reactivity in the Brain

项目摘要 脑肿瘤、癫痫或其他疾病手术患者脑功能区的功能定位 神经系统疾病是预防手术后缺陷和降低发病率的关键。基于任务(TB)的功能 磁共振成像(FMRI)，它检测患者在进行手术时血氧水平依赖(BOLD)信号的变化 任务范例，是一种标准的护理临床程序，用于术前绘制有说服力的皮质。两大 临床TB-fMRI的局限性是患者无法执行任务和病变导致的功能障碍 神经血管耦合(驱动大胆信号)。静息状态(RS)功能磁共振成像，用于测量同步 休息时的大胆信号振荡，可用于绘制大脑网络图，对患者的要求最低 符合标准，并已被证明可以准确地定位运动和语言区域，以进行术前规划。 脑血管反应性(CVR)映射，在高碳酸血症任务期间通过动态BOLD成像访问 屏气扫描可用于识别神经血管可能导致的fMRI结果假阴性的区域 解偶联(NVU)，并已被建议作为一种新兴的标准用于临床功能磁共振。目前， 没有商业上可用的FDA批准的软件工具来本地化休眠状态网络(RSN) 或者CVR。临床研究人员依赖的研究软件包要么没有在临床上整合 或者尚未在大量患者群体中进行优化和验证。因此，迫切需要一个经过审查的软件解决方案 使这些最先进的功能磁共振成像方法能够使患者超越TB-功能磁共振成像的限制而受益。我们 假设增强、优化和验证我们的初步软件并将其与 已建立的商业功能磁共振成像平台将为RSN和CVR的临床标测创建强大的解决方案。穿过 三个具体目标，软件解决方案将使用来自 大约350名脑瘤或癫痫患者在三家机构接受治疗。目标1是为以下目标创建软件 绘制RSN并确定用于本地化主要视觉、运动等有说服力区域的优化工作流 (手、舌和脚)和语言(主要和次要)区域。基于种子的相关性和 独立成分分析将被纳入。目标2是创建绘制CVR和CVR的软件 确定用于识别和可视化潜在假阴性功能磁共振脑区的优化工作流程 结果。该软件将包括多延迟通用线性模型和独特的图形用户界面 想像NVU的样子。目标3是使用术前功能磁共振数据集测试和验证该软件。结果将会是 与(1)使用广泛使用的研究软件包处理的结果进行比较，(2)TB- FMRI，以及(3)术中直接皮质刺激。这项研究有望创造出强有力的临床应用 可用的软件将极大地增加可以受益于术前功能磁共振的患者群体，并将 提高手术计划中功能定位的信心。这将直接使患者受益，因为 他们的手术后功能，同时允许外科医生安全地最大限度地切除脑部病变。

Project Summary Functional localization of eloquent brain areas for patients undergoing surgery for brain tumors, epilepsy, or other neurological diseases is crucial to prevent post-surgical deficits and reduce morbidity. Task-based (tb) functional MRI (fMRI), which detects blood oxygenation level–dependent (BOLD) signal changes while a patient performs task paradigms, is a standard-of-care clinical procedure for presurgical mapping of eloquent cortices. Two major limitations of clinical tb-fMRI are a patient's inability to perform the task and lesion-induced impairment of neurovascular coupling (which drives the BOLD signal). Resting-state (rs) fMRI, which measures synchronized BOLD signal oscillations during rest, can be used to map brain networks with minimal requirements for patient compliance and has been demonstrated to accurately localize motor and language areas for presurgical planning. Cerebrovascular reactivity (CVR) mapping, accessed by dynamic BOLD imaging during a hypercapnia task such as breath-holding, can be used to identify areas with potential false-negative fMRI results due to neurovascular uncoupling (NVU) and has been suggested as an emerging standard to be used with clinical fMRI. Currently, there are no commercially available FDA-cleared software tools for localizing the resting-state networks (RSNs) or CVR. Clinical investigators have relied on research software packages that are either not clinically integrated or not yet optimized and validated in large patient populations. Thus, a vetted software solution is urgently needed to enable these state-of-the-art fMRI methods to benefit patients beyond the limitations of tb-fMRI. We hypothesize that enhancing, optimizing, and validating our preliminary software and integrating it with an established commercial fMRI platform will create robust solutions for clinical mapping of RSN and CVR. Through three specific aims, the software solutions will be optimized and tested with rs-fMRI and CVR datasets from approximately 350 patients with brain tumors or epilepsy at three institutions. Aim 1 is to create the software for mapping RSNs and determine optimized workflows for localizing eloquent areas including primary visual, motor (hands, tongue, and feet), and language (primary and secondary) areas. Both seed-based correlation and independent component analysis will be incorporated. Aim 2 is to create the software for mapping CVR and determine the optimized workflow for identifying and visualizing brain areas with potential false-negative fMRI results. The software will include a multiple-latency general linear model and a unique graphical user interface to visualize the NVU. Aim 3 is to test and validate the software with presurgical fMRI datasets. The results will be compared against those obtained from (1) processed using widely used research software packages, (2) tb- fMRI, and (3) intraoperative direct cortical stimulation. This research is anticipated to create robust and clinically available software that will greatly increase the patient population who can benefit from presurgical fMRI and will improve confidence in functional localization for surgical planning. This will directly benefit patients by preserving their post-surgical functions while allowing surgeons to safely maximize the resection of brain lesions.