Drug addiction can cause abnormal brain activation changes, which are the root cause of drug craving and brain function errors. This study enrolled drug abusers to determine the effects of different drugs on brain activation. A functional near-infrared spectroscopy (fNIRS) device was used for the research. This study was designed with an experimental paradigm that included the induction of resting and drug addiction cravings. We collected the fNIRS data of 30 drug users, including 10 who used heroin, 10 who used Methamphetamine, and 10 who used mixed drugs. First, using Statistical Analysis, the study analyzed the activations of eight functional areas of the left and right hemispheres of the prefrontal cortex of drug addicts who respectively used heroin, Methamphetamine, and mixed drugs, including Left/Right-Dorsolateral prefrontal cortex (L/R-DLPFC), Left/Right-Ventrolateral prefrontal cortex (L/R-VLPFC), Left/Right-Frontopolar prefrontal cortex (L/R-FPC), and Left/Right Orbitofrontal Cortex (L/R-OFC). Second, referencing the degrees of activation of oxyhaemoglobin concentration (HbO2, the study made an analysis and got the specific activation patterns of each group of the addicts. Finally, after taking out data which are related to the addicts who recorded high degrees of activation among the three groups of addicts, and which had the same channel numbers, the paper classified the different drug abusers using the data as the input data for Convolutional Neural Networks (CNNs). The average three-class accuracy is 67.13%. It is of great significance for the analysis of brain function errors and personalized rehabilitation.

脑血流中的血红蛋白有两种: 氧合血红蛋白(HbO2)和还原血红蛋白(HbR)。 这两种血红蛋白在脑血流中浓度的变化可以反应脑部神经活动, 提取其浓度变化信号可以为如癫痫病灶定位、 抑郁等相关疾病的诊断和治疗提供依据和参考。 目前, 使用近红外光谱提取脑血流信号的算法有EEMD-ICA法主成分分析法(PCA)、 独立成分分析法(ICA)、 相干平均法、 自适应滤波等, 这些算法在对近红外脑神经活动信号提取时都有各自的特点和优势, 但都重视如呼吸、 眼动等各种生理干扰, 忽视了测量过程中符合高斯分布的测量干扰, 如仪器精密度、 信号传输中的串扰等。 为了提取脑血流中氧合血红蛋白(HbO2)和还原血红蛋白(HbR)浓度变化信号, 设计了功能性近红外光谱(fNIRS)的脑血流参数采集装置, 选择波长为750和830 nm的二极管近红外光源采集脑部血流变化信号, 采用扩展的卡尔曼滤波(EKF)算法, 把生理干扰和测量干扰建立对应的数学模型, 使用基于误差平方和最小的原理进行递归计算, 通过对下一时刻系统的初步状态估计以及测量得出的反馈相结合, 得到该时刻无限逼近真实值的状态估计, 结合修正的朗伯比尔定律(Lambert-Beer law), 将光密度信号的变化转换为氧合血红蛋白(HbO2)和还原血红蛋白(HbR)浓度变化信号。 结果表明: 所提方法可以有效去除符合高斯分布的测量干扰, 在Valsava实验和视觉诱发实验中, 可以提取出脑血流中氧合血红蛋白(HbO2)和还原血红蛋白(HbR)浓度变化曲线, 和主流的EEMD提取脑信号算法比对其RMSE值提高了0.96%, r值提高了0.6%, 表明提出的方法有一定的优越性。 所提方法为相关脑部疾病诊断等提供了有效的脑神经活动探测方法。

Functional near-infrared spectroscopy (fNIRS), a growing neuroimaging modality, has been utilized over the past few decades to understand the neuronal behavior in the brain. The technique has been used to assess the brain hemodynamics of impaired cohorts as well as able-bodied. Neuroimaging is a critical technique for patients with impaired cognitive or motor behaviors. The portable nature of the fNIRS system is suitable for frequent monitoring of the patients who exhibit impaired brain activity. This study comprehensively reviews brain-impaired patients: The studies involving patient populations and the diseases discussed in more than 10 works are included. Eleven diseases examined in this paper include autism spectrum disorder, attentionde ficit hyperactivity disorder, epilepsy, depressive disorders, anxiety and panic disorder, schizophrenia, mild cognitive impairment, Alzheimer's disease, Parkinson's disease, stroke, and traumatic brain injury. For each disease, the tasks used for examination, fNIRS variables, and significant findings on the impairment are discussed. The channel configurations and the regions of interest are also outlined. Detecting the occurrence of symptoms at an earlier stage is vital for better rehabilitation and faster recovery. This paper illustrates the usability of fNIRS for early detection of impairment and the usefulness in monitoring the rehabilitation process. Finally, the limitations of the current fNIRS systems (i.e., nonexistence of a standard method and the lack of well-established features for classification) and future research directions are discussed. The authors hope that the findings in this paper would lead to advanced breakthrough discoveries in the fNIRS field in the future.

The aim of this study is to examine the small-world properties of functional brain networks in Chinese to English simultaneous interpreting (SI) using functional near-infrared spectroscopy (fNIRS). In particular, the fNIRS neuroimaging combined with complex network analysis was performed to extract the features of functional brain networks underling three translation strategies associated with Chinese to English SI: “transcoding" that takes the “shortcut" linking translation equivalents between Chinese and the English, “code-mixing" that basically does not involve bilingual processing, and “transphrasing" that takes the “long route" involving a monolingual processing of meaning in Chinese and then another monolingual processing of meaning in English. Our results demonstrated that the small-world network topology was able to distinguish well between the transcoding, code-mixing and transphrasing strategies related to Chinese to English SI.grants from the Macau government.

Recent studies have suggested a link between executive function (EF) and obesity. Studies often adopt body mass index (BMI), which reflects the distribution of subcutaneous fat, as the sole marker of obesity; however, BMI is inappropriate to distinguish central obesity, which indicates the centralized distribution of visceral fat. Visceral fat compared with subcutaneous fat represents greater relative lipid turnover and may increase the risk of cognitive decline in older adults. However, the relationship between EF and central obesity is largely unknown, particularly in young adults. Therefore, we used waist circumference (WC) as a marker of central obesity and investigated different sensitivities between BMI and WC in the brain function. A total of 26 healthy young adults (aged 18 25 years; 42% female) underwent functional near-infrared spectroscopy assessments. EF was assessed using the Stroop task, which is a classical measurement of EF. A significant Stroop effect was observed in the behavioral and hemodynamic data. In addition, we observed that behavioral interference on the Stroop task varied much more in subjects with higher BMI and WC than those subjects with lower. Elevated BMI and WC were associated with a decreased hemodynamic response during the Stroop task specifically in the prefrontal cortex (PFC). Compared to BMI, WC was more closely connected with inhibitory control and revealed right lateralized PFC activation. Our findings suggest that WC is a reliable indicator of brain function in young adults and propose a relationship between EF and central obesity.

近年来, 功能性近红外光谱技术(fNIRS)广泛应用于神经影像学领域。 为解决fNIRS特征信号提取中的信噪频谱混叠问题, 依据近红外光谱脑功能成像信号非线性与非平稳特点, 提出一种结合集合经验模态分解法和独立成分分析的多分辨率联合信号提取方法EEMD-ICA。 在脑功能成像仪器平台上采集多通道多波长脑功能成像近红外光密度信号, 先对该信号进行集合经验模态分解将其按频率成分分解为多层本征模态函数, 之后将独立成分分析应用于目标频率分量函数进行自适应去噪, 最后将处理后的分量累加、 重构获得近红外光谱脑功能成像的特征信号。 将Valsalva氏实验测试数据作为研究对象进行滤噪处理, 与经验模态分解法和集合经验模态分解法对fNIRS特征信号的提取效果对比。 对实测数据的处理结果进行信噪比和误差参数分析, 结果表明, 该方法能够有效解决去噪过程中丢失原始信号有用信息及由于信噪频谱混叠不能完整去除噪声的问题, 信号处理效果理想, 对比另外两种信号提取方法更为优化。

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technology which is suitable for psychiatric patients. Several fNIRS studies have found abnormal brain activations during cognitive tasks in elderly depression. In this paper, we proposed a discriminative model of multivariate pattern classification based on fNIRS signals to distinguish elderly depressed patients from healthy controls. This model used the brain activation patterns during a verbal fluency task as features of classification. Then Pseudo-Fisher Linear Discriminant Analysis was performed on the feature space to generate discriminative model. Using leave-one-out (LOO) cross-validation, our results showed a correct classification rate of 88%. The discriminative model showed its ability to identify people with elderly depression and suggested that fNIRS may be an efficient clinical tool for diagnosis of depression. This study may provide the first step for the development of neuroimaging biomarkers based on fNIRS in psychiatric disorders.

利用功能近红外光谱技术(functionality near infrared spectroscopy, fNIRs)探索帕金森病(parkinson′s disease, PD)大鼠模型的脑组织功能特性。 通过小动物磁共振(magnetic resonance imaging, MRI)和电子计算机断层扫描(computed tomography, CT)对PD大鼠模型进行影像学研究, 用fNIRs系统测试大鼠模型脑组织纹状体特征参数。 实验结果表明, PD大鼠脑部没有明显的形态结构变化； 优化散射系数(reduced scattering coefficient: μ′s)、 脑血容量(cerebral blood volume: CBV)在PD大鼠的纹状体部与对照组间存在显著的差别； fNIRs测量参数(μ′s、 CBV)与CT灌注(CTP)测定参数［CBF(cerebral blood flow), CBV］之间存在相关性。 这些结果表明fNIRs可以作为PD研究的重要参考手段。

Working memory is one of the most important functions in our brain, which has been widely studied with unreal-life measured technologies. A functional near-infrared spectroscopy (fNIRS) instrument with a portable and low-cost design is developed, which is capable of providing hemodynamic measurement associated with brain function in real-life situations. Using this instrument, we performed working memory studies involved in Chinese words encoding, verbal, and spatial stem recognition, which are mainly studied with other technologies. Our results show that fNIRS can well assess working memory activities, in comparison with the reported results mainly using other methodologies. Furthermore, we find that hemodynamic change in the prefrontal cortex during all working memory tasks is highly associated with subjects’ behavioral data. fNIRS is shown to be a promising alternative to the current methodologies for studying or assessing functional brain activities in natural condition.