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

项目摘要 接受脑肿瘤、癫痫或其他手术的患者的功能脑区的功能定位 神经系统疾病是预防术后缺陷和降低发病率的关键。基于任务的功能 磁共振成像（fMRI），它检测血氧水平依赖（BOLD）信号的变化，而病人执行 任务范例是用于功能皮层的术前映射的护理标准临床程序。两大 临床tb-fMRI的局限性是患者无法执行任务和损伤引起的功能障碍。 神经血管耦合（驱动BOLD信号）。静息状态（rs）功能磁共振成像，测量同步 休息期间的BOLD信号振荡，可用于映射大脑网络，对患者的要求最低 顺应性，并已被证明可以准确定位运动和语言区域，用于术前规划。 脑血管反应性（CVR）标测，在高碳酸血症任务期间通过动态BOLD成像进行， 与屏气一样，可以用来识别由于神经血管疾病而导致的潜在假阴性fMRI结果的区域。 解偶联（NVU），并已被建议作为一个新兴的标准，用于临床功能磁共振成像。目前， 目前还没有经过美国食品药品管理局批准的商用软件工具来对静止状态网络进行本地化 或CVR。临床研究者依赖于研究软件包，这些软件包要么没有临床集成， 或者尚未在大量患者群体中优化和验证。因此，迫切需要一个经过审查的软件解决方案。 需要使这些最先进的功能磁共振成像方法能够使患者受益，超越tb-fMRI的局限性。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fMRI平台将为RSN和CVR的临床映射创建强大的解决方案。通过 三个具体的目标，软件解决方案将进行优化，并与rs-fMRI和CVR数据集测试，从 大约350名脑瘤或癫痫患者在三个机构。目标1是创建软件， 绘制RSN并确定优化的工作流程，用于定位功能区，包括主要视觉、运动 （手，舌头和脚）和语言（主要和次要）区域。基于种子的相关性和 将纳入独立成分分析。目标2是创建用于映射CVR的软件， 确定识别和可视化潜在假阴性fMRI脑区的优化工作流程 结果该软件将包括一个多延迟一般线性模型和一个独特的图形用户界面 来可视化NVU。目的3是使用术前fMRI数据集测试和验证软件。结果将 与从以下方法获得的数据进行比较：（1）使用广泛使用的研究软件包进行处理，（2）结核病- fMRI;（3）术中直接皮层刺激。这项研究预计将创造强大的和临床 可用的软件，这将大大增加患者人群谁可以受益于术前功能磁共振成像，并将 提高手术计划功能定位的信心。这将使患者直接受益， 它们的术后功能，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.