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

全面的临床 fMRI 软件解决方案，可绘制大脑中的关键功能网络和脑血管反应性

• 批准号: 10595033
• 负责人: CATHERINE L ELSINGER
• 金额: $ 37.37万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-21 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10595033
• 关键词: Area; Blood Vessels; Brain; Brain Mapping; Brain Neoplasms; Brain imaging; Clinical; Clinical Investigator; Communities; Computer software; Data; Data Set; Disease; Ensure; Environment; Epilepsy; Excision; Failure; Functional Magnetic Resonance Imaging; Hand; Hypercapnia; Image; Impairment; Institution; Language; Lesion; Linear Models; Magnetic Resonance Imaging; Malignant neoplasm of brain; Maps; Measurement; Measures; Methods; Morbidity - disease rate; Motor; Navigation System; Neoplasms; Neurosciences; Neurosciences Research; Operative Surgical Procedures; Pathology; Patients; Procedures; Process; Research; Rest; Seasons; Signal Transduction; Software Tools; Software Validation; Surgeon; Technology; Testing; Tissues; Tongue; Visual; Visualization; Work; blood oxygen level dependent; blood oxygenation level dependent imaging; brain surgery; brain tumor resection; cerebrovascular; clinical care; compliance behavior; flexibility; foot; graphical user interface; imaging detection; improved; independent component analysis; innovation; malformation; nervous system disorder; neurosurgery; neurovascular; neurovascular coupling; novel; patient population; preservation; prevent; software development; standard of care; tool; visual map

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.

项目总结