From the Division of Neuroradiology, the Russell H. Morgan Department of Radiology and Radiological Science (E.B., R.J., A.M.S., S.A., J.J.P., S.K.G., H.I.S.) and the Department of Neurosurgery (J.J.P.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD 21287; Division of Neuroradiology, Department of Radiology, Temple University Hospital, Philadelphia, Pa (V.S.); Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Md (G.W., M.A.L., B.S.C.); and the Malone Center for Engineering in Healthcare, the Whiting School of Engineering, Johns Hopkins University, Baltimore, Md (H.I.S.).
Radiology. 2021 Oct;301(1):178-184. doi: 10.1148/radiol.2021204594. Epub 2021 Jul 20.
Background Resting-state functional MRI (rs-fMRI) is a potential alternative to task-based functional MRI (tb-fMRI) for somatomotor network (SMN) identification. Brain networks can also be generated from tb-fMRI by using independent component analysis (ICA). Purpose To investigate whether the SMN can be identified by using ICA from a language task without a motor component, the sentence completion functional MRI (sc-fMRI) task, compared with rs-fMRI. Materials and Methods The sc-fMRI and rs-fMRI scans in patients who underwent presurgical brain mapping between 2012 and 2016 were analyzed, using the same imaging parameters (other than scanning time) on a 3.0-T MRI scanner. ICA was performed on rs-fMRI and sc-fMRI scans with use of a tool to separate data sets into their spatial and temporal components. Two neuroradiologists independently determined the presence of the dorsal SMN (dSMN) and ventral SMN (vSMN) on each study. Groups were compared by using tests, and logistic regression was performed to identify predictors of the presence of SMNs. Results One hundred patients (mean age, 40.9 years ± 14.8 [standard deviation]; 61 men) were evaluated. The dSMN and vSMN were identified in 86% (86 of 100) and 76% (76 of 100) of rs-fMRI scans and 85% (85 of 100) and 69% (69 of 100) of sc-fMRI scans, respectively. The concordance between rs-fMRI and sc-fMRI for presence of dSMN and vSMN was 75% (75 of 100 patients) and 53% (53 of 100 patients), respectively. In 10 of 14 patients (71%) where rs-fMRI did not show the dSMN, sc-fMRI demonstrated it. This rate was 67% for the vSMN (16 of 24 patients). Conclusion In the majority of patients, independent component analysis of sentence completion task functional MRI scans reliably demonstrated the somatomotor network compared with resting-state functional MRI scans. Identifying target networks with a single sentence completion scan could reduce overall functional MRI scanning times by eliminating the need for separate motor tasks. © RSNA, 2021 . See also the editorial by Field and Birn in this issue.
背景
静息态功能磁共振成像(rs-fMRI)是一种有潜力的替代任务态功能磁共振成像(tb-fMRI)的方法,可用于识别躯体运动网络(SMN)。通过独立成分分析(ICA)也可以从 tb-fMRI 生成脑网络。
目的
本研究旨在通过使用不包含运动成分的句子完成功能磁共振成像(sc-fMRI)任务,从静息态功能磁共振成像(rs-fMRI)中识别躯体运动网络,与 rs-fMRI 进行比较。
材料与方法
对 2012 年至 2016 年间行术前脑映射的患者的 sc-fMRI 和 rs-fMRI 扫描进行分析,在 3.0T 磁共振扫描仪上使用相同的成像参数(除扫描时间外)。采用一种工具将 rs-fMRI 和 sc-fMRI 扫描的数据集分离为其空间和时间成分,对其进行 ICA。两位神经放射科医生独立确定每项研究中背侧躯体运动网络(dSMN)和腹侧躯体运动网络(vSMN)的存在情况。使用 t 检验比较组间差异,采用 logistic 回归识别 SMN 存在的预测因素。
结果
共 100 例患者(平均年龄,40.9 岁±14.8[标准差];61 例男性)入组。rs-fMRI 扫描中 86%(86/100)和 76%(76/100)例患者可识别出 dSMN 和 vSMN,sc-fMRI 扫描中分别为 85%(85/100)和 69%(69/100)例患者可识别出 dSMN 和 vSMN。rs-fMRI 和 sc-fMRI 对 dSMN 和 vSMN 存在的一致性分别为 75%(75/100 例患者)和 53%(53/100 例患者)。14 例患者(rs-fMRI 未显示 dSMN)中,10 例(71%)患者的 sc-fMRI 显示出 dSMN,vSMN 为 24 例患者中 16 例(67%)。
结论
在大多数患者中,与 rs-fMRI 相比,句子完成任务功能磁共振成像扫描的独立成分分析更可靠地显示躯体运动网络。使用单次句子完成扫描识别目标网络可以减少整体功能磁共振成像扫描时间,无需单独进行运动任务。
©2021RSNA。本期杂志中还可见 Field 和 Birn 的社论。