Lu Junfeng, Zhang Han, Hameed N U Farrukh, Zhang Jie, Yuan Shiwen, Qiu Tianming, Shen Dinggang, Wu Jinsong
Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Sci Rep. 2017 Oct 23;7(1):13769. doi: 10.1038/s41598-017-14248-5.
As a noninvasive and "task-free" technique, resting-state functional magnetic resonance imaging (rs-fMRI) has been gradually applied to pre-surgical functional mapping. Independent component analysis (ICA)-based mapping has shown advantage, as no a priori information is required. We developed an automated method for identifying language network in brain tumor subjects using ICA on rs-fMRI. In addition to standard processing strategies, we applied a discriminability-index-based component identification algorithm to identify language networks in three different groups. The results from the training group were validated in an independent group of healthy human subjects. For the testing group, ICA and seed-based correlation were separately computed and the detected language networks were assessed by intra-operative stimulation mapping to verify reliability of application in the clinical setting. Individualized language network mapping could be automatically achieved for all subjects from the two healthy groups except one (19/20, success rate = 95.0%). In the testing group (brain tumor patients), the sensitivity of the language mapping result was 60.9%, which increased to 87.0% (superior to that of conventional seed-based correlation [47.8%]) after extending to a radius of 1 cm. We established an automatic and practical component identification method for rs-fMRI-based pre-surgical mapping and successfully applied it to brain tumor patients.
作为一种非侵入性且“无需任务”的技术，静息态功能磁共振成像（rs-fMRI）已逐渐应用于术前功能图谱绘制。基于独立成分分析（ICA）的图谱绘制已显示出优势，因为无需先验信息。我们开发了一种使用rs-fMRI上的ICA在脑肿瘤受试者中识别语言网络的自动化方法。除了标准处理策略外，我们应用基于可辨别性指数的成分识别算法在三个不同组中识别语言网络。训练组的结果在一组独立的健康人类受试者中得到验证。对于测试组，分别计算ICA和基于种子点的相关性，并通过术中刺激图谱评估检测到的语言网络，以验证在临床环境中的应用可靠性。除一名受试者外，两个健康组的所有受试者均可自动实现个性化语言网络图谱绘制（19/20，成功率=95.0%）。在测试组（脑肿瘤患者）中，语言图谱结果的敏感性为60.9%，在扩展到1厘米半径后提高到87.0%（优于传统的基于种子点的相关性[47.8%]）。我们建立了一种基于rs-fMRI的术前图谱绘制的自动实用成分识别方法，并成功将其应用于脑肿瘤患者。
