A preliminary study of functional abnormalities in aMCI subjects during different episodic memory tasks

Functional magnetic resonance imaging (fMRI) is an important imaging modality to understand the neurodegenerative course of mild cognitive impairment (MCI) and early Alzheimer's disease (AD), because the memory dysfunction may occur before structural degeneration is obvious. In this research, we investigated the functional abnormalities of subjects with amnestic MCI (aMCI) using three episodic memory paradigms that are relevant to different memory domains in both encoding and recognition phases. Both whole-brain analysis and region-of-interest (ROI) analysis of the medial temporal lobes (MTL), which are central to the memory formation and retrieval, were used to compare the efficiency of the different memory paradigms and the functional difference between aMCI subjects and normal control subjects. We also investigated the impact of using different functional activation measurements in ROI analysis. This pilot study could facilitate the use of fMRI activations in the MTL as a marker for early detection and monitoring progression of AD.     

多面转镜激光器扫频光学相干层析成像系统的全光谱重采样方法

在多面转镜激光器扫频光学相干层析成像系统中,激光器存在着输出光谱错位与扫频范围波动的问题.目前的重采样方法中,普遍利用互相关运算校正光谱错位,并进行大范围的截取,保证扫频范围的一致性,但这会导致成像信噪比与分辨率的降低.本文用马赫-曾德尔干涉仪(MZI)采集到的干涉信号对扫频范围波动的问题进行了详细的测量与分析,其中干涉信号的解缠相位曲线的非随机性和平行性,表明该类激光器输出光谱的波长分布具备一致性.在此基础上,提出了一种用最长扫频范围的MZI干涉信号,对样品干涉信号进行时域光谱对齐、然后进行一对多插值的重采样方法.实验与分析表明,该方法利用了所有的光谱信号,保证了样品干涉信号的能量利用率,能有效提高图像的信噪比与分辨率.     

Longitudinal stability of resting-state networks in normal aging,mild cognitive impairment,and Alzheimer's disease

Test–retest reliability is essential for using resting-state functional magnetic resonance imaging (rs-fMRI) as a potential biomarker for Alzheimer's disease (AD), especially when monitoring longitudinal changes and treatment effects. In addition, test–retest variability itself might represent a feature of AD. Using 3.0 T rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we examined the long-term (1-year) test–retest reliability of resting-state networks (RSNs) in 31 healthy elderly subjects, 63 patients with mild cognitive impairment (MCI), and 17 patients with AD by applying temporal concatenation group independent component analysis and dual regression. The intraclass correlation coefficient estimates of RSN amplitudes ranged from 0.44 to 0.77 in healthy elderly subjects, from 0.31 to 0.62 in patients with MCI, and from −0.06 to 0.44 in patients with AD. The overall test–retest reliability of RSNs was lower in patients with MCI than in healthy elderly subjects, and was lower in patients with AD than in patients with MCI. The differences in the test–retest reliabilities were due to the RSN amplitudes rather than the RSN shapes. Head motion was not significantly different among the three groups of subjects. The results indicate that the test–retest stability of RSNs generally declines with progression to MCI and AD, mainly due to the RSN amplitudes rather than the RSN shapes. The test–retest instability in MCI and AD may reflect progressive neurofunctional alterations related to the pathology of AD.     

Stochastic Resonance in Linear Region of a Single-Mode Laser： Effects of Amplitude Modulation of Signal

A single-mode laser noise model driven by quadratic colored pump noise and amplitude modulation signal is proposed. The real and imaginary parts of the pump noise are assumed to be cross-correlation. The effect of cross-correlation of noise and amplitude modulation of signal on laser statistical properties is studied by using the linearized approximation. The analytic expression of signal-to-noise ratio (SNR) is calculated. It is found that the phenomena of stochastic resonance (SR) respectively exist in the curves of the SNR versus the noise cross-correlation coefficient λ and the SNR versus the pump parameter a, as well as the SNR versus the signal frequency ω in our model. It is shown that there are three different typies of SR in the model: the conventional form of SR, the SR in the broad sense, and the bona fide SR.     

Stochastic resonance in a gain--noise model of a single-mode laser driven by pump noise and quantum noise with cross-correlation between real and imaginary parts under direct signal modulation

Stochastic resonance (SR) is studied in a gain--noise model of a single-mode laser driven by a coloured pump noise and a quantum noise with cross-correlation between real and imaginary parts under a direct signal modulation. By using a linear approximation method, we find that the SR appears during the variation of signal-to-noise ratio (SNR) separately with the pump noise self-correlation time \tau , the noise correlation coefficient between the real part and the imaginary part of the quantum noise \lambda_q , the attenuation coefficient \gamma and the deterministic steady-state intensity I_0 . In addition, it is found that the SR can be characterized not only by the dependence of SNR on the noise variables of \tau and \lambda_q, but also by the dependence of SNR on the laser system variables of \gamma and I₀. Thus our investigation extends the characteristic quantity of SR proposed before.     

Influences of quantum noises on direct-modulated properties of 1.3-μm InGaAsP/InP laser diodes

Due to the zero dispersion point at 1.3μm in optical fibres, 1.3-μm InGaAsP/InP laser diodes have become main light sources in fibre communication systems recently. Influences of quantum noises on direct-modulated properties of single-mode 1.3-μm InGaAsP/InP laser diodes are investigated in this article. Considering the carrier and photon noises and the cross-correlation between the two noises, the power spectrum of the photon density and the signal-to-noise ratio (SNR) of the direct-modulated single-mode laser system are calculated using the linear approximation method. We find that the stochastic resonance (SR) always appears in the dependence of the SNR on the bias current density, and is strongly affected by the cross-correlation coefficient between the carrier and photon noises, the frequency of modulation signal, and the photon lifetime in the laser cavity. Hence, it is promising to use the SR mechanism to enhance the SNR of direct-modulated InGaAsP/InP laser diodes and improve the quality of optical fibre communication systems.     

孔径扩展中相位修正因子估计的改进算法

传统的扩展拖曳阵列尺寸算法(Extended Towed Array Measurement,ETAM)在信噪比不够高、相位修正因子的相位角处于以间断点为中心的"跳变区间"时,扩展孔径均有可能失效。针对传统的ETAM算法中互相关相位角的间断点导致的算法不稳定问题,提出了一种相位修正因子估计的改进算法。该算法使用归一化互相关复向量的统计平均值作为相位修正因子的最小二乘估计,消除了互相关相位角的间断点带来的不利影响。数值仿真和实验数据分析结果表明:对于相位稳定甚至相位随机的单目标信号,改进算法均能有效扩展孔径,获得相对于常规波束形成(Conventional Beamforming,CBF)更高的方位分辨率和检测信噪比;相比于传统的ETAM算法,改进算法提高了相位修正因子的估计准确性,从而有效提高了算法的稳定性。      

Stochastic Resonance in a Single-Mode Laser Driven by Pump Noise and Quantum Noise with Cross-Correlated Real and Imaginary Parts

We present an analytic investigation of the signal-to-noise ratio (SNR) by studying a signal modulated model of a single-mode laser system driven by pump noise and quantum noise with correlated real and imaginary parts,and find there is a maximum in the curve of the dependence of SNR upon the cross-correlation coefficient λq between the real part and the imaginary part, i.e., stochastic resonance appears in the SNR vs. λq curve. Moreover, when the SNR is at the maximum, the cross-correlation coefficient λq = O, which is coincidentally at the minimum of the mean normalized intensity fluctuation. The influences on stochastic resonance by the intensities of the pump and the quantum noise, the amplitude of the modulation signal, and the net gain of the laser are also studied. Furthermore, in order to ensure that the results obtained in this paper is reliable, the valid range for the linear approximation method is discussed.     

Controlling the false positive rate in fuzzy clustering using randomization: application to fMRI activation detection

Despite its potential advantages for fMRI analysis, fuzzy C-means (FCM) clustering suffers from limitations such as the need for a priori knowledge of the number of clusters, and unknown statistical significance and instability of the results. We propose a randomization-based method to control the false-positive rate and estimate statistical significance of the FCM results. Using this novel approach, we develop an fMRI activation detection method. The ability of the method in controlling the false-positive rate is shown by analysis of false positives in activation maps of resting-state fMRI data. Controlling the false-positive rate in FCM allows comparison of different fuzzy clustering methods, using different feature spaces, to other fMRI detection methods. In this article, using simulation and real fMRI data, we compare a novel feature space that takes the variability of the hemodynamic response function into account (HRF-based feature space) to the conventional cross-correlation analysis and FCM using the cross-correlation feature space. In both cases, the HRF-based feature space provides a greater sensitivity compared to the cross-correlation feature space and conventional cross-correlation analysis. Application of the proposed method to finger-tapping fMRI data, using HRF-based feature space, detected activation in sub-cortical regions, whereas both of the FCM with cross-correlation feature space and the conventional cross-correlation method failed to detect them.     

Simultaneous pulse carving misalignment and pi-phase-shift monitoring of a return-to-zero differential phase-shift keying system using a coherent hybrid

A technique for monitoring both the pulse carving misalignment and pi phase shift in a return-to-zero (RZ) differential phase-shift keying (DPSK) system based on a phase modulator is proposed and demonstrated. By monitoring the mean-power variation of a hybrid local oscillation lightwave with the modulated signal lightwave without any deliberated control, the technique can provide the information on pulse carving misalignment and phase modulation index simultaneously, as the power variation reflects the asymmetric distribution of RZ-DPSK in a signal constellation diagram. It is characterized as transparent to signal bit rate, central wavelength, and absolute power level. Both the simulation and experiment results show that more than +/- 15% bit duration misalignment and +/- 5% phase shift deviated from pi can be detected.     

SNR and functional sensitivity of BOLD and perfusion-based fMRI using arterial spin labeling with spiral SENSE at 3 T

Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) studies using parallel imaging to reduce the readout window have reported a loss in temporal signal-to-noise ratio (SNR) that is less than would be expected given a purely thermal noise model. In this study, the impact of parallel imaging on the noise components and functional sensitivity of both BOLD and perfusion-based fMRI data was investigated. Dual-echo arterial spin labeling data were acquired on five subjects using sensitivity encoding (SENSE), at reduction factors (R) of 1, 2 and 3. Direct recording of cardiac and respiratory activity during data acquisition enabled the retrospective removal of physiological noise. The temporal SNR of the perfusion time series closely followed the thermal noise prediction of a √R loss in SNR as the readout window was shortened, with temporal SNR values (relative to the R=1 data) of 0.72 and 0.56 for the R=2 and R=3 data, respectively, after accounting for physiological noise. However, the BOLD temporal SNR decreased more slowly than predicted even after accounting for physiological noise, with relative temporal SNR values of 0.80 and 0.63 for the R=2 and R=3 data, respectively. Spectral analysis revealed that the BOLD trends were dominated by low-frequency fluctuations, which were not dominant in the perfusion data due to signal processing differences. The functional sensitivity, assessed using mean F values over activated regions of interest (ROIs), followed the temporal SNR trends for the BOLD data. However, results for the perfusion data were more dependent on the threshold used for ROI selection, most likely due to the inherently low SNR of functional perfusion data.     

Temperature sensitivity of Ge-B-SiO(2) waveguide Bragg gratings on a crystallized glass substrate

Waveguide Bragg gratings were fabricated by plasma-enhanced chemical-vapor deposition followed by irradiation with KrF excimer laser light through a phase mask. The period of the Bragg grating was 0.53 mum, and the Bragg wavelength was ~1.53 mum . The temperature dependence of the Bragg wavelength was 11 pm/ degrees C for a 10GeO(2) -90SiO(2)(mol.%) core waveguide on a Si substrate, and the Bragg wavelength shift was successfully reduced to 5.0pm/ degrees C by use of a 14GeO(2)-12B(2)O(3)-74SiO(2) (mol.%) core and a crystallized glass substrate with a thermal-expansion coefficient of -2.0x10(-6)(/ degrees C) .     

Integration of structural and functional magnetic resonance imaging improves mild cognitive impairment detection

The identification of mild cognitive impairments (MCI) via either structural magnetic resonance imaging (sMRI) or functional MRI (fMRI) has great potential due to the non-invasiveness of the techniques. Furthermore, these techniques allow longitudinal follow-ups of single subjects via repeated measurements. sMRI- or fMRI-based biomarkers have been adopted separately to diagnose MCI; however, there has not been a systematic effort to integrate sMRI- and fMRI-based features to increase MCI detection accuracy. This study investigated whether the detection of MCI can be improved via the integration of biomarkers identified from both sMRI and fMRI modalities. Regional volume sizes and neuronal activity levels of brains from MCI subjects were compared with those from healthy controls and used to identify biomarkers from sMRI and fMRI data, respectively. In the subsequent classification phase, MCI was automatically detected using a support vector machine algorithm that employed the identified sMRI- and fMRI-based biomarkers as an input feature vector. The results indicate that the fMRI-based biomarkers provided more information for detecting MCI than the sMRI-based biomarkers. Moreover, the integrated feature sets using the sMRI- and fMRI-based biomarkers consistently showed greater detection accuracy than the feature sets based only on the fMRI-based biomarkers. The results demonstrate that integration of sMRI and fMRI modalities can provide supplemental information to improve the diagnosis of MCI relative to either the sMRI or fMRI modalities alone.     

Stochastic resonance in single-mode semiconductor lasers

Owing to the considerable virtues of semiconductor lasers for applications, they have become the main optical source for fiber communication systems recently. The behavior of stochastic resonance (SR) in direct-modulated semiconductor laser systems is investigated in this article. Considering the carrier and photon noises and the cross-correlation between the two noises, the power spectrum of the photon density and the signal-to-noise ratio (SNR) of the modulated laser system were calculated using the linear approximation method. We found that the SR always appears in the dependence of the SNR upon the bias current density, and is strongly affected by the cross-correlation coefficient of the carrier and photon noises, the frequency of modulation signal and the photon lifetime in the laser cavity. Hence, it is promising to use the SR mechanism to enhance the SNR of direct-modulated semiconductor laser systems and improve the quality of optical communication.     

Individual identification using multi-metric of DTI in Alzheimer's disease and mild cognitive impairment

Accurate identification of Alzheimer's disease(AD) and mild cognitive impairment(MCI) is crucial so as to improve diagnosis techniques and to better understand the neurodegenerative process. In this work, we aim to apply the machine learning method to individual identification and identify the discriminate features associated with AD and MCI. Diffusion tensor imaging scans of 48 patients with AD, 39 patients with late MCI, 75 patients with early MCI, and 51 age-matched healthy controls(HCs) are acquired from the Alzheimer's Disease Neuroimaging Initiative database. In addition to the common fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity metrics, there are two novel metrics,named local diffusion homogeneity that used Spearman's rank correlation coefficient and Kendall's coefficient concordance,which are taken as classification metrics. The recursive feature elimination method for support vector machine(SVM)and logistic regression(LR) combined with leave-one-out cross validation are applied to determine the optimal feature dimensions. Then the SVM and LR methods perform the classification process and compare the classification performance.The results show that not only can the multi-type combined metrics obtain higher accuracy than the single metric, but also the SVM classifier with multi-type combined metrics has better classification performance than the LR classifier.Statistically, the average accuracy of the combined metric is more than 92% for all between-group comparisons of SVM classifier. In addition to the high recognition rate, significant differences are found in the statistical analysis of cognitive scores between groups. We further execute the permutation test, receiver operating characteristic curves, and area under the curve to validate the robustness of the classifiers, and indicate that the SVM classifier is more stable and efficient than the LR classifier. Finally, the uncinated fasciculus, cingulum, corpus callosum, corona radiate, external capsule, and internal capsule have been regarded as the most important white matter tracts to identify AD, MCI, and HC. Our findings reveal a guidance role for machine-learning based image analysis on clinical diagnosis.     

Influence of the net gain on characteristic of stochastic resonance in a single-mode laser system

The phenomenon of stochastic resonance (SR) is found in a single-mode laser system driven by the colored pump noise with signal modulation and the quantum noise with cross-correlation between the real and imaginary parts. When the net gain a0 changes, it is found that, 1) the shape of the curve of the signal-tonoise ratio (SNR) versus the pump noise self-correlation time T exhibits a changing process of multiform SR, from single-peak SR to simultaneous existence of resonances and suppressions; 2) the curve of SNR versus signal frequency Ω experiences a complicated changing process from the monotonous descending to the simultaneous appearances of a maximum and a minimum, and finally to monotonous descending; 3)the curve of SNR versus cross-correlation coefficient between the real and imaginary parts of the quantum noise λq appears an acute single-peak SR. Therefore, the net gain a0 greatly influences the characteristic of SR of laser system.     

色关联噪声驱动下双模激光随机共振

运用线性近似方法,计算得到了关联色噪声和输入周期信号作用下双模激光增益模型输出信号光强的功率密度谱和信噪比,讨论了信噪比随系统参数的变化关系.研究结果发现:在系统的参数和输入信号的频率满足一定条件时,信噪比不仅随噪声强度变化出现了传统的随机共振现象,还发现其随自饱和系数c2、输入信号频率°及交叉耦合参数b的变化都出现了随机共振.     

A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer's disease classification

PurposeAlzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. In recent years, machine learning methods have been widely used on analysis of neuroimage for quantitative evaluation and computer-aided diagnosis of AD or prediction on the conversion from mild cognitive impairment (MCI) to AD. In this study, we aimed to develop a new deep learning method to detect or predict AD in an efficient way.Materials and methodsWe proposed a densely connected convolution neural network with connection-wise attention mechanism to learn the multi-level features of brain MR images for AD classification. We used the densely connected neural network to extract multi-scale features from pre-processed images, and connection-wise attention mechanism was applied to combine connections among features from different layers to hierarchically transform the MR images into more compact high-level features. Furthermore, we extended the convolution operation to 3D to capture the spatial information of MRI. The features extracted from each 3D convolution layer were integrated with features from all preceding layers with different attention, and were finally used for classification. Our method was evaluated on the baseline MRI of 968 subjects from ADNI database to discriminate (1) AD versus healthy subjects, (2) MCI converters versus healthy subjects, and (3) MCI converters versus non-converters.ResultsThe proposed method achieved 97.35% accuracy for distinguishing AD patients from healthy control, 87.82% for MCI converters against healthy control, and 78.79% for MCI converters against non-converters. Compared with some neural networks and methods reported in recent studies, the classification performance of our proposed algorithm was among the top ranks and improved in discriminating MCI subjects who were in high risks of conversion to AD.ConclusionsDeep learning techniques provide a powerful tool to explore minute but intricate characteristics in MR images which may facilitate early diagnosis and prediction of AD.     

Functional MRI using super-resolved spatiotemporal encoding

Recently, new ultrafast imaging sequences such as rapid acquisition by sequential excitation and refocusing (RASER) and hybrid spatiotemporal encoding (SPEN) magnetic resonance imaging (MRI) have been proposed, in which the phase encoding of conventional echo planar imaging (EPI) is replaced with a SPEN. In contrast to EPI, SPEN provides significantly higher immunity to frequency heterogeneities including those caused by B₀ inhomogeneities and chemical shift offsets. Utilizing the inherent robustness of SPEN, it was previously shown that RASER can be used to successfully perform functional MRI (fMRI) experiments in the orbitofrontal cortex — a task which is challenging using EPI due to strong magnetic susceptibility variation near the air-filled sinuses. Despite this superior performance, systematic analyses have shown that, in its initial implementation, the use of SPEN was penalized by lower signal-to-noise ratio (SNR) and higher radiofrequency power deposition as compared to EPI-based methods. A recently developed reconstruction algorithm based on super-resolution principles is able to alleviate both of these shortcomings; the use of this algorithm is hereby explored within an fMRI context. Specifically, a series of fMRI measurements on the human visual cortex confirmed that the super-resolution algorithm retains the statistical significance of the blood oxygenation level dependent (BOLD) response, while significantly reducing the power deposition associated with SPEN and restoring the SNR to levels that are comparable with those of EPI.     

变分模态分量降噪后重构的水声弱信号检测

针对能量检测法在低信噪比下对非合作水声探测信号的检测性能显著下降的问题,提出了一种组合变分模态分解和小波变换降噪重构的信号检测方法。以信号分解出的各个本征模态函数的近似熵与互相关系数比值作为分量分类参数,将所得分量分为信号分量、含噪信号分量与噪声分量,然后利用第二代小波变换对含噪信号分量降噪后与信号分量组成重构信号,最后对重构信号进行检测。数值仿真结果表明该方法可以在无先验信息的情况下对CW和LFM信号自适应降噪,信噪比0 dB以下时CW信号重构后信噪比提升约12 dB,宽带LFM信号信噪比提升约8～9 dB,有效提升了低虚警概率下信号的检测概率。湖试结果表明,虚警概率为0.1时检测概率可提升至0.9以上,验证了该方法的有效性。