Impact of sampling rate on statistical significance for single subject fMRI connectivity analysis

A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, the impact of the autocorrelation is largely ignored. Recently, autocorrelation has been addressed by variance correction approaches, which are sensitive to the sampling rate. In this article, we aim to investigate the impact of the sampling rate on the variance correction approaches. Toward this end, we first derived a generalized expression for the variance of the sample Pearson correlation coefficient (SPCC) in terms of the sampling rate and the filter cutoff frequency, in addition to the autocorrelation and cross‐covariance functions of the time series. Through simulations, we illustrated the importance of the variance correction for a fixed sampling rate. Using the real resting state fMRI data sets, we demonstrated that the data sets with higher sampling rates were more prone to false positives, in agreement with the existing empirical reports. We further demonstrated with single subject results that for the data sets with higher sampling rates, the variance correction strategy restored the integrity of true connectivity.     

Visualization of electrophysiological activity at the carpal tunnel area using magnetoneurography

ObjectiveTo establish a noninvasive method to measure the neuromagnetic fields of the median nerve at the carpal tunnel after electrical digital nerve stimulation and evaluate peripheral nerve function.MethodsUsing a vector-type biomagnetometer system with a superconducting quantum interference device, neuromagnetic fields at the carpal tunnel were recorded after electrical stimulation of the index or middle digital nerve in five healthy volunteers. A novel technique for removing stimulus-induced artifacts was applied, and current distributions were calculated using a spatial filter algorithm and superimposed on X-ray.ResultsA neuromagnetic field propagating from the palm to the carpal tunnel was observed in all participants. Current distributions estimated from the magnetic fields had five components: leading and trailing components parallel to the conduction pathway, outward current preceding the leading component, inward currents between the leading and trailing components, and outward current following the trailing component. The conduction velocity and peak latency of the inward current agreed well with those of sensory nerve action potentials.ConclusionRemoving stimulus-induced artifacts enabled magnetoneurography to noninvasively visualize with high spatial resolution the electrophysiological neural activity from the palm to the carpal tunnel.SignificanceThis is the first report of using magnetoneurography to visualize electrophysiological nerve activity at the palm and carpal tunnel.     

移动协同办公在病区物资管理中的应用

目的利用智能手机软件辅助实现病区物资的移动智能化管理。方法将病区物资分类整理并制作成Excel工作表,通过智能手机和微信、WPS office手机软件功能进行工作表编辑、查询定时提醒、共享、权限设置,以实现病区物资移动智能化管理。结果通过该应用,病区建立了高效低耗的物资管理制度,物资管理缺陷率明显下降,实施前后差异有统计学意义(P<0.05)。结论基于移动协同办公进行病区物资管理是可行的,获得院内临床护士一致认可,具有临床推广价值。     

Geographical Origin and Level Identification of Frankincense Based on Hyperspectral Image

Background: As the demand for traditional Chinese medicinal materials increases in China and even the world, there is an urgent need for an effective and simple identification technology to identify the origin and quality of the latter and ensure the safety of clinical medication. Mineral element analysis and isotope finger-printing are the two commonly used techniques in traditional origin identification. Both of these techniques require the use of stoichiometric methods in the identification process. Although they have high accuracy and sensitivity, they are expensive and inefficient. In addition, near-infrared spectroscopy is a fast, nondestructive, and widely used identification technique developed in recent years, but its identification results are susceptible to samples’ states and environmental conditions, and its sensitivity is low. Hyperspectral imaging combines the advantages of imaging technology and optical technology, which can simultaneously access the image information and spectral information which reflect the external characteristics, internal physical structure, and chemical composition of the samples. Hyperspectral imaging is widely applied to agricultural product inspection, but research into its application in origin and quality identification of TCM materials is rare. Methods: In this study, the algorithm framework discriminative marginalized least squares regression (DMLSR) was used for feature extraction of frankincense hyperspectral data. The DMLSR with intraclass compactness graph and manifold regularization can efficiently learn the projective samples with higher separability and less redundant information than the original samples. Then, the discriminative collaborative representation with Tikhonov regularization (DCRT) was applied for classifying the geographical origin and level of frankincense. DCRT introducesthe discriminant regularization term and incorporates SID, which is more sensitive to the spectrum as the measurement method and is more suitable for the frankincense spectral data compared with SVM. Results: For the origin classification task, samples of all levels from each origin were, respectively, selected for three?way classification. We used 10-fold cross-validation to select a model parameter in the experiment. When obtaining the optimal parameters, we randomly selected the training set and testing set, where the training set accounts for 70% and the training set for 30%. After repeating this random process 10 times, we obtained the final average classification accuracy, which is higher than 90%, and the standard deviation fluctuation is usually small. For the level classification task, samples of each level from three origins were separately selected for multiclassification. We randomly selected the training set and testing set from each origin. The level classification results of the three origins are good on D4350 data, and the classification accuracy of each level is basically above 80%. Conclusion: Experiments and analysis show that our algorithm framework has excellent classification performance, which is stable in origin classification and has potential for generalization. In addition, the experiments show that in our algorithm framework, different classification tasks need to combine different data sources to achieve better classification and recognition, as the origin classification task uses frankincense’s D3000 data, and level classification task uses frankincense’s D4350 data.     

血氧测量快速滤波与实时算法设计

目的血氧饱和度是人体的重要参数,为适应无线监护系统的需要,针对无线血氧测量节点内存小、运算速度慢的特点,本文设计了一套递推型快速处理算法。方法该算法充分利用递推方式和形态滤波算法过程的中间结果,加速滤波算法的处理速度,提取出测量和控制的关键数据,保证了数据的快速处理和测量系统的实时控制。结果通过分析采样数据和模拟处理过程发现,该算法可有效消除基线漂移、抖动等干扰,保证测量的稳定可靠。结论该算法计算量小,抗干扰能力强,适合无线血氧测量节点实时测量的需求。