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Superficial Fluctuations in Functional Near-Infrared Spectroscopy

机译:功能近红外光谱功能浅表波动

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Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical functional neuroimaging that has seen rapid development and increasing use in studying human brain under normal and diseased conditions. Compared with blood-oxygenation-level dependent functional magnetic resonance imaging (BOLD fMRI), fNIRS offers advantages including its low cost, portability and compatibility with implanted medical devices. Thus, fNIRS can be used to monitor brain activity particularly in infants, elders and patients who are unable to undergo routine fMRI scans. However, fNIRS suffers from its susceptibility to scalp and to systemic physiological noises. Fluctuations originated from heartbeat, respiration and low-frequency oscillations lead to contamination of cerebral activity. In order to tap the full potential of fNIRS, it is essential to eliminate these confounding noises from fNIRS measurements. Therefore, the present study aims to understand the underlying relationship between superficial signals and the compound signals respectively measured by short channels and long channels of fNIRS optodes in a whole head configuration. Our results reveal that: 1) 49.56% of total variances in long-channel data are contributed by a global component shared across all long channels; 2) this global component is significantly correlated with the superficial fluctuations extracted from short-channel data. Finally, our findings indicate that compound signals measured by long channels of fNIRS are contaminated by superficial fluctuations and that careful removal of these fluctuations from long-channel data is critical in obtaining accurate images of cerebral activity with fNIRS.
机译:功能近红外光谱(FNIR)是一种非侵入性光学功能神经模光,在正常和患病条件下,在研究人脑时已经看到了快速发展和日益增加。与血氧级依赖性功能磁共振成像(粗体FMRI)相比,FNIR提供了其低成本,便携性和与植入医疗设备的兼容性的优点。因此,FNIR可用于监测脑活动,特别是在无法接受常规FMRI扫描的婴儿,长老和患者中。然而,FNIRs源于其对头皮的易感性以及系统性的生理噪音。起源于心跳,呼吸和低频振荡的波动导致脑活动的污染。为了挖掘Fnirs的全部潜力,必须从FNIR测量中消除这些混杂噪声。因此,本研究旨在了解浅表信号和分别通过全头部配置的短通道和长通道测量的浅表信号和复合信号之间的基础关系。我们的研究结果表明:1)长通道数据中的总差异的49.56%是通过所有长渠道共享的全球组件的贡献; 2)该全局组分与短信数据中提取的浅表波动显着相关。最后,我们的研究结果表明,通过浅表波动测量的由长通道测量的化合物信号被浅表波动污染,仔细地移除来自长信程数据的这些波动对于获得具有Fnirs的脑活动的准确图像至关重要。

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