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.     

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。     

Unifying account of visual motion and position perception

Despite growing evidence for perceptual interactions between motion and position, no unifying framework exists to account for these two key features of our visual experience. We show that percepts of both object position and motion derive from a common object-tracking system—a system that optimally integrates sensory signals with a realistic model of motion dynamics, effectively inferring their generative causes. The object-tracking model provides an excellent fit to both position and motion judgments in simple stimuli. With no changes in model parameters, the same model also accounts for subjects’ novel illusory percepts in more complex moving stimuli. The resulting framework is characterized by a strong bidirectional coupling between position and motion estimates and provides a rational, unifying account of a number of motion and position phenomena that are currently thought to arise from independent mechanisms. This includes motion-induced shifts in perceived position, perceptual slow-speed biases, slowing of motions shown in visual periphery, and the well-known curveball illusion. These results reveal that motion perception cannot be isolated from position signals. Even in the simplest displays with no changes in object position, our perception is driven by the output of an object-tracking system that rationally infers different generative causes of motion signals. Taken together, we show that object tracking plays a fundamental role in perception of visual motion and position.Research into the basic mechanisms of visual motion processing has largely focused on simple cases in which motion signals are fixed in space and constant over time (e.g., moving patterns presented in static windows) (1). Although this approach has resulted in considerable advances in our understanding of low-level motion mechanisms, it leaves open the question of how the brain integrates changing motion and position signals; when objects move in the world, motion generally co-occurs with changes in object position. The process of generating coherent estimates of object motion and position is known in the engineering and computer vision literature as “tracking” (e.g., as used by the Global Positioning System) (2). Conceptualizing motion and position perception in the broader context of object tracking suggests an alternative conceptual framework—one that we show provides a unifying account for a number of perceptual phenomena.An optimal tracking system would integrate incoming position and motion signals with predictive information from the recent past to continuously update perceptual estimates of both an object’s position and its motion. Were such a system to underlie perception, position and motion should be perceptually coupled in predictable ways. Signatures of such a coupling appear in a number of known phenomena. On one hand, local motion signals can predictively bias position percepts (3–8). On the other hand, we can perceive motion solely from changes in object position (9–12). For example, motion can be perceived in stimuli with no directional motion signal by tracking position changes along a specific direction (10). These phenomena, however, are currently regarded as arising from independent mechanisms (11–14).Given the interdependency of motion and position and the inherent noisiness of sensory signals, it is advantageous for vision to exploit the redundancy between motion and position signals and integrate them into coupled perceptual estimates. This is complicated by the fact that local motion signals can result from a combination of motions (of which object translations are only one) (15, 16). A flying, rotating soccer ball provides a prototypical example of this problem (Fig. 1A). Because the ball rotates as it flies through the air, the local retinal motion signals created by ball texture are sums of two world motions: translation and rotation of the ball. Relating local motion signals to object motion requires the solution of the “source attribution” problem (17, 18)—determining what part of a local retinal motion pattern is due to object translation and what part is due to object-relative motion of the texture pattern. To solve this attribution problem, the brain can exploit the redundant information provided by the changing stimulus position. Moreover, integrating motion and position information over time with an internal model of motion dynamics can mitigate both the uncertainty created by ubiquitous sensory noise (19) and that created by the motion source attribution problem. Although object-relative pattern motion is not a property of all moving objects, understanding how pattern motion interacts with object motion and position can help elucidate how the brain integrates motion and position signals into coherent perceptual estimates—a problem associated with all moving objects.Open in a separate windowFig. 1.Schematic illustration of the object-tracking model and its behavior. (A) An example of an object with both object boundary motion and pattern motion. (B) A generative model of the Bayesian observer. White nodes indicate hidden variables and gray nodes indicate observable variables that are noisy measurements of the connected hidden variables. Arrows indicate causal links. (C) Model behavior for a typical MIPS stimulus containing a moving pattern within a static envelope. The steady-state estimates of the three object states (position, object velocity, and pattern velocity) are plotted for different positional uncertainties. At low positional uncertainty, most of the retinal texture motion is correctly attributed to the pattern motion. Consequently, illusory object motion and MIPS are negligible. At high positional uncertainty, much of the texture motion is attributed to object motion (reflecting a prior that object motion is more likely than pattern motion). This results in relatively low estimated pattern velocity and large MIPS.Here, we propose and test a computational framework in which motion and position perception derive from a common mechanism that integrates sensory signals over time to track objects and infer their generative causes. The consequence of this process is a strong, bidirectional coupling between motion and position perception that provides a unifying account for a range of perceptual phenomena. These include motion-induced shifts in perceived position (3–6), perceptual speed biases (20), slowing of motions shown in visual periphery (21, 22), and the curveball illusion (16). The presented model also makes novel predictions about interactions between position and motion perception—predictions confirmed here. Importantly, we do not fit the model to each experiment, but fit the parameters to data from experiment 1 and show that the resulting model accurately predicts subjects’ performance in qualitatively different and more complex tasks (experiments 2 and 3).     

Batch to batch optimal control based on multiinput multioutput adaptive hinging hyperplanes prediction and Kalman filter correction

A batch to batch optimal control strategy based on multiinput multioutput adaptive hinging hyperplanes (MIMO AHH) prediction and Kalman filter correction is proposed for the products quality control of the batch process. The model of AHH is one kind of piecewise linear models and is extended to the MIMO case in this article. The MIMO AHH is then used to develop the predictive model of the batch process. Due to the model-plant mismatch and unknown disturbances, the optimal control policy calculated based on the MIMO AHH predictive model may not be optimal when applied to the true process. The Kalman filter is then utilized to correct the predictions of the current batch by considering the information of former batches. The effectiveness of the proposed strategy is verified through the simulation of a styrene batch polymerization reactor.     

基于微区域PM2.5浓度卡尔曼插值预测模型的研究

目前颗粒物(尤其是PM2.5)污染问题日趋严重,人们对其关注度越来越高。本文提出一种结合三次样条插值方法的卡尔曼预测模型并将其应用于微区域校园环境PM2.5浓度的预测,以及实现PM2.5浓度的插值模拟图,模拟PM2.5的空间分布。本文实验基于实验室已搭建的环境信息监测系统服务器数据,其PM2.5浓度数据预测值和实际值通过Wilcoxon带符号秩检验后,双侧渐进显著性概率为0.527,远大于显著性水平α=0.05。同时,与神经网络模型预测方法(BP预测)和支持向量机预测方法(SVM预测)对比,卡尔曼预测模型的结果更理想,其日均值PM2.5浓度数据预测值和监测值的平均绝对误差(MEA)为1.8μg/m3,平均相对误差(MER)为6%,相关系数R为0.87。实验结果表明:卡尔曼预测模型能有效地用于PM2.5浓度预测,结合样条插值方法可以较好地模拟PM2.5的空间分布及局部污染特征。     

Interframe clutter filtering for high frequency flow imaging

This study investigates 'interframe' clutter filtering with a high frequency (HF) flow imaging system with the objective of improving the performance of HF microvascular imaging at high frame rates. An interframe filter exploits the correlation of tissue signals on the time scale of the frame rate and is, therefore, insensitive to tissue spectral broadening induced by sweeping a single element transducer over a region of tissue. In vitro experiments were conducted in a tissue-mimicking flow phantom over a range of mean flow velocities (0.5 to 70.0 mm/s). Power Doppler (PD) imaging and color flow (CF) imaging were performed for both slow (0.25 fps) and fast (20 fps) scanning acquisitions. Flow data acquired at 20 fps and interframe filtered had similar velocity and mean Doppler power values as the 0.25 fps single-frame filtered data sets. In vivo validation experiments were conducted using a 500 microm blood vessel in a human finger and detected blood flow of 2 to 3 mm/s. Further in vivo experiments examining experimental murine tumors demonstrated the feasibility of performing HF PD and CF imaging at high frame rates using interframe filtering.     

Adaptive self-calibrating iterative GRAPPA reconstruction

Parallel magnetic resonance imaging in k-space such as generalized auto-calibrating partially parallel acquisition exploits spatial correlation among neighboring signals over multiple coils in calibration to estimate missing signals in reconstruction. It is often challenging to achieve accurate calibration information due to data corruption with noises and spatially varying correlation. The purpose of this work is to address these problems simultaneously by developing a new, adaptive iterative generalized auto-calibrating partially parallel acquisition with dynamic self-calibration. With increasing iterations, under a framework of the Kalman filter spatial correlation is estimated dynamically updating calibration signals in a measurement model and using fixed-point state transition in a process model while missing signals outside the step-varying calibration region are reconstructed, leading to adaptive self-calibration and reconstruction. Noise statistic is incorporated in the Kalman filter models, yielding coil-weighted de-noising in reconstruction. Numerical and in vivo studies are performed, demonstrating that the proposed method yields highly accurate calibration and thus reduces artifacts and noises even at high acceleration.