fMRI与DTI在神经科学研究中的联合应用

功能磁共振成像(fMRI)和扩散张量成像(DTI)是近年来磁共振成像领域出现的两种新的成像技术,它们各具特色。功能磁共振成像能对人脑相关任务激活区进行准确的功能定位并提供相关皮层区域的磁共振信号改变特征信息,但时于脑白质相关改变则不能提供任何信息;扩散张量成像则是目前能够在体呈现人脑解剖连接的唯一手段,采用它能对人脑组织,包括灰质和白质的扩散特性进行定量研究,并且能够形象显示人脑生理或病理状态下的纤维束形态、走行等,但扩散张量成像不能提供皮层功能情况信息。功能磁共振成像和扩散张量成像技术具有很强的互补性,二者联合在神经科学研究中具有广阔的应用前景。目前也正成为神经科学研究领域的热点之一。本文从功能磁共振成像和扩散张量成像的原理、特点,二者结合应用的具体方法以及目前二者在神经科学各基础及临床学科结合应用的研究进展进行了综述。     

头部组织三维核磁共振电阻抗成像算法的仿真研究

文章给出了一种基于核磁共振技术的三维阻抗成像（电导率分布）重构算法,并将该方法应用于人体头部组织电导率分布重构上。该代数重构方法是利用高分辨率的核磁共振成像系统对成像物体进行三维构建和不同组织的边界区分,根据核磁共振系统中测量得到的磁感应强度Bx和By分量并结合有限元数值计算得到的电流密度分布J组成非线性矩阵,通过迭代求解此非线性矩阵,来解决三维电导率分布的重构问题。在三层球头模型（包括头皮、颅骨和大脑）上分别进行的仿真实验结果表明,该算法具有较强的抗噪声能力和较好的收敛性,重构的头部电导率分布图像具有较高的精确性。     

活体荧光成像对裸鼠肝癌细胞系MHCC97-H原位移植模型的动态量化分析

目的:利用生物自发光的裸鼠肝癌原位移植模型,以活体荧光成像技术对肝癌的生长和转移情况进行动态、量化分析.方法:将稳定转染了荧光素酶(luciferase)基因的人肝癌细胞株MHCC97-H-LUC细胞,移植至裸鼠肝脏包膜下,每周利用活体荧光成像系统对裸鼠体内移植瘤的生长部位和范围进行成像,测量肿瘤细胞生物发光量,动态观察肝癌细胞在裸鼠体内的肿瘤数量、生长速度和转移情况.结果:建立可稳定表达荧光素酶的人肝癌细胞株MHCC97-H-LUC并用于进行生物自发光的裸鼠原位移植模型;利用活体荧光成像系统对裸鼠体内的移植瘤成像,见发光部位由肝脏向腹腔扩散,发光量随时间呈指数级增长;病理学观察证实肿瘤细胞长.结论:利用活体荧光成像技术的动态量化分析可灵敏、准确地监测裸鼠肝癌原位移植模型中肿瘤细胞的生长及转移情况,为肿瘤发生、生长、转移机制及对抗肿瘤生长和转移的体内研究提供了科学的量化手段.     

凋落叶空间扩散模型在常绿阔叶林的适用性分析

利用凋落叶空间扩散模型研究单株植物的凋落叶扩散过程,这对预测凋落叶在地表的分布格局有重要意义.本文依据浙江天童20 hm²动态监测样地植被调查数据和叶凋落量数据,分别对20种目标树种进行凋落叶空间扩散模型的拟合,以及模型适用性分析.模型假设叶凋落量和植株胸径之间服从异速生长关系,并且叶凋落量随距离呈指数降低,通过极大似然法估计模型参数.结果表明： 所有树种实际叶凋落量和理论叶凋落量相关性显著;但树种间的模型预测精度相差较大,各树种理论叶凋落量解释实际叶凋落量变异的百分比为16.0%～74.0%,平均为49.3%.模型预测精度与叶凋落量数据的标准差、树种平均胸径、树种平均叶片干质量呈显著正相关.根据各树种的分布格局,使凋落物筐覆盖到不同胸径母树周围不同距离处,确定各树种的最优扩散函数,以及不断改进已有的扩散函数可以提高模型的预测精度.     

人脑功能连通性研究进展

对人脑结构和功能的深入研究,已经要求脑成像技术不能仅仅局限于研究简单的脑功能定位问题,即寻找和定位与特定认知任务相关的某一块或者一组大脑皮层功能区,而必须研究分析各功能区间的动态功能连通和整合问题,即描述特定脑功能区域间的交互作用以及这些交互作用如何受认知任务的影响.已有几种非常规的脑成像技术和数据分析方法,包括时间相关性分析、心理生理交互作用(PPI)、结构方程模型(SEM)、动态因果模型(DCM)、弥散张量成像(DTI)等等,被成功用于人脑功能连通性和有效连通性的研究.脑功能连通性研究的发展,有利于深入理解人脑在系统水平上的动态运作方式,是今后认知神经科学发展的一个重要方向.     

蛋白质电导率的跃迁机理及其应用

总结了近年来关于蛋白质电导率的理论研究工作,简要介绍和评述了过去已有的对蛋白质电导率的研究工作,讨论了对蛋白质分子作完整分子的电子结构和理论电导率计算的近似方案和计算方法,由计算结果总结出蛋白质的电子结构和理论电导率的基本特性．最后,根据对胰岛素的计算结果提出了胰岛素及其受体在跨膜信号传递中的电子通道模型．     

生物组织电导率图像的热声重建

本文以微波热声效应为基础,报道了一种重建生物组织电导率相对分布图像的方法。和传统的微波热声像相比,重建得到的组织电导率图像消除了由于微波场能量密度分布不均匀带来的影响,能够准确的反映组织的介电特性差异,实现对肿瘤的早期发现。本文首先从理论上详细推导了获得生物组织电导率分布图像的原理,然后在实验中通过结合已有的脉冲微波成像系统,重建得到了一块肌肉组织的电导率分布图像。实验结果表明利用热声成像技术,组织的电导率分布图像能够被清晰的重建,本研究为推进热声成像技术的实用性打下了理论和实验基础。     

气孔下腔间的水汽扩散

本文把气孔及其下腔看作截面为椭圆形的柱形区域,提出一个水汽从气孔下腔内所有细胞表面扩散到气孔外端的三维扩散模型。根据 Fick 定律和质量守恒定律建立了支配该模型的水汽扩散方程。用有限差分法,借助于计算机求得水汽从气孔下腔的所有细胞表面扩散到气孔内端所遇到的阻力及其近似表达式。并从理论上对该阻力的倒数——导度随气孔面积而变化的方式做了分析和解释。通过将本模型求得的气孔下腔阻力计算公式与 Brown 等以及 Cooke 的公式比较,发现在气孔开度变化相当大的范围内用后面两公式计算的阻力偏大0.5—1倍左右。此外,计算结果还表明:在气孔下腔水散失总量中,腔内表皮细胞表面上的水散失量占86—96%,而保卫细胞表面上的水散失量又占后者的88—93%,副卫细胞表面上的水散失量仅7—12%。     

基于神经网络原理的荧光寿命成像研究

荧光寿命成像技术(fhlorescence lifetime imaging,FLIM)是一种新颖且功能强大的、能用于复杂生物组织和细胞结构与功能分析的生物组织成像技术。传统的时域荧光寿命成像数据分析方法,由于没有考虑荧光分子团之间以及他们与周围环境的相互作用,可能导致复杂的连续分布荧光寿命这一实际情况,因此对生物组织中自发荧光发光强度衰减过程的实验数据拟合效果欠佳。文章提出利用人工神经网络(artificial neural network,ANN)原理拟合算法来计算生物荧光分子团衰减动力过程,该方法能有效地建立生物荧光分子团衰减动力过程的非线性模型,并且具有处理非线性模型能力强、鲁棒性好、拟合精度高和所需计算时间少等优点。通过计算证明,相对于单参量指数与多参量指数衰减函数,这种数据拟合方法对于某些荧光分子团的多槽基面效价测定样品(multi-well plate assays)的数据有更好的一致性和更小的计算量。同时在文章中讨论了将该拟合算法应用于荧光寿命成像的前景。     

一种低成本、高性价比的穿戴式扩散光学断层人脑成像系统

现有扩散光学断层成像(diffuse optical tomography,DOT)系统常采用高档光探测器和放大器,令系统成本居高不下、性价比较低.本文旨在使用全新技术降低系统成本、增加成像深度,研发一套可实用于人脑成像的新型扩散光学断层成像系统.为此采用了成本较低的新型光探测器和一款自主设计研制的光电二极管前置放大器.相较于实验室原有DOT系统,新系统整体成本节约了40%以上.其次,设计了一款具有弹性并可适应多种不规则待测物(subject)外形的无光纤穿戴式脑-机接口(brain computer interface,BCI)装置.本文首次将手持式3D激光扫描仪应用于待测物外形结构和光源-探测器位置信息的精确获取.最后综合应用上述技术装备于人脑形状仿体成像实验中,新系统的目标成像深度提升至35 mm.验证结果说明本系统已具备了实际人脑功能成像能力.     

A model for diffusion in white matter in the brain

Diffusion of molecules in brain and other tissues is important in a wide range of biological processes and measurements ranging from the delivery of drugs to diffusion-weighted magnetic resonance imaging. Diffusion tensor imaging is a powerful noninvasive method to characterize neuronal tissue in the human brain in vivo. As a first step toward understanding the relationship between the measured macroscopic apparent diffusion tensor and underlying microscopic compartmental geometry and physical properties, we treat a white matter fascicle as an array of identical thick-walled cylindrical tubes arranged periodically in a regular lattice and immersed in an outer medium. Both square and hexagonal arrays are considered. The diffusing molecules may have different diffusion coefficients and concentrations (or densities) in different domains, namely within the tubes' inner core, membrane, myelin sheath, and within the outer medium. Analytical results are used to explore the effects of a large range of microstructural and compositional parameters on the apparent diffusion tensor and the degree of diffusion anisotropy, allowing the characterization of diffusion in normal physiological conditions as well as changes occurring in development, disease, and aging. Implications for diffusion tensor imaging and for the possible in situ estimation of microstructural parameters from diffusion-weighted MR data are discussed in the context of this modeling framework.     

Optimization of in vivo high-resolution DTI of non-human primates on a 3T human scanner

Magnetic resonance (MR) diffusion tensor imaging (DTI) has emerged as a unique technique to reveal small anatomical structures of brain by characterizing the diffusion process of water molecules within an image voxel. Combined with fiber tractography techniques, DTI can be further used to reveal white matter fibers and connectivity in the brain non-invasively. The non-human primate brain study provides important supplemental means for human brain exploration since the two species share close anatomical and functional similarities. There is therefore increasing interest in in vivo non-human primate DTI studies. However, several technical challenges need to be addressed to perform non-human primate brain DTI and fiber tractography. We have established an imaging protocol together with a post-acquisition procedure for high-resolution in vivo non-human primate DTI studies using a 3T human clinical scanner. Data acquired with this procedure is appropriate for accurate diffusion tensor quantification and fiber tractography, and is accessible within an acceptable scan time. We investigated in detail the effects of spatial resolution and SNR on diffusion tensor-derived quantities and fiber tractography. Our results should be of general utility for implementation of in vivo non-human primate DTI studies.     

Low‐frequency conductivity tensor of rat brain tissues inferred from diffusion MRI

Conductivity tensor maps of the rat brain were obtained using diffusion magnetic resonance imaging (MRI). Signal attenuations in the cortex and the corpus callosum were measured using the stimulated echo acquisition mode (STEAM) sequence with b factors up to 6000 s/mm². Our previously published method was improved to infer 3 × 3 conductivity tensor at the low‐frequency limit. The conductivity tensor of the tissue was inferred from the fast component of the diffusion tensor and a fraction of the fast component. The mean conductivity (MC) of the cortex and the corpus callosum was 0.52 and 0.62 S/m, respectively. Diffusion‐weighted images were obtained with b factors up to 4500 s/mm². Conductivity tensor images were calculated from the fast diffusion tensor images. Tissues with highly anisotropic cellular structures, such as the corpus callosum, the internal capsule, and the trigeminal nerve, exhibited high anisotropy in conductivity. The resulting values corresponded to conductivities at the low‐frequency limit because our method assumed electric currents flowing only through extracellular fluid. Bioelectromagnetics 30:489–499, 2009. © 2009 Wiley‐Liss, Inc.     

White matter pathways in reading

Skilled reading requires mapping of visual text to sound and meaning. Because reading relies on neural systems spread across the brain, a full understanding of this cognitive ability involves the identification of pathways that communicate information between these processing regions. In the past few years, diffusion tensor imaging has been used to identify correlations between white matter properties and reading skills in adults and children. White matter differences have been found in left temporo-parietal areas and in posterior callosal tracts. We review these findings and relate them to possible pathways that are important for various aspects of reading. We describe how the results from diffusion tensor imaging can be integrated with functional results in good and poor readers.     

Computational Study of Subdural Cortical Stimulation: Effects of Simulating Anisotropic Conductivity on Activation of Cortical Neurons

Subdural cortical stimulation (SuCS) is an appealing method in the treatment of neurological disorders, and computational modeling studies of SuCS have been applied to determine the optimal design for electrotherapy. To achieve a better understanding of computational modeling on the stimulation effects of SuCS, the influence of anisotropic white matter conductivity on the activation of cortical neurons was investigated in a realistic head model. In this paper, we constructed pyramidal neuronal models (layers 3 and 5) that showed primary excitation of the corticospinal tract, and an anatomically realistic head model reflecting complex brain geometry. The anisotropic information was acquired from diffusion tensor magnetic resonance imaging (DT-MRI) and then applied to the white matter at various ratios of anisotropic conductivity. First, we compared the isotropic and anisotropic models; compared to the isotropic model, the anisotropic model showed that neurons were activated in the deeper bank during cathodal stimulation and in the wider crown during anodal stimulation. Second, several popular anisotropic principles were adapted to investigate the effects of variations in anisotropic information. We observed that excitation thresholds varied with anisotropic principles, especially with anodal stimulation. Overall, incorporating anisotropic conductivity into the anatomically realistic head model is critical for accurate estimation of neuronal responses; however, caution should be used in the selection of anisotropic information.     

A linear model for characterization of synchronization frequencies of neural networks

The synchronization frequency of neural networks and its dynamics have important roles in deciphering the working mechanisms of the brain. It has been widely recognized that the properties of functional network synchronization and its dynamics are jointly determined by network topology, network connection strength, i.e., the connection strength of different edges in the network, and external input signals, among other factors. However, mathematical and computational characterization of the relationships between network synchronization frequency and these three important factors are still lacking. This paper presents a novel computational simulation framework to quantitatively characterize the relationships between neural network synchronization frequency and network attributes and input signals. Specifically, we constructed a series of neural networks including simulated small-world networks, real functional working memory network derived from functional magnetic resonance imaging, and real large-scale structural brain networks derived from diffusion tensor imaging, and performed synchronization simulations on these networks via the Izhikevich neuron spiking model. Our experiments demonstrate that both of the network synchronization strength and synchronization frequency change according to the combination of input signal frequency and network self-synchronization frequency. In particular, our extensive experiments show that the network synchronization frequency can be represented via a linear combination of the network self-synchronization frequency and the input signal frequency. This finding could be attributed to an intrinsically-preserved principle in different types of neural systems, offering novel insights into the working mechanism of neural systems.     

Diffusion MRI of complex neural architecture

While functional brain imaging methods can locate the cortical regions subserving particular cognitive functions, the connectivity between the functional areas of the human brain remains poorly understood. Recently, investigators have proposed a method to image neural connectivity noninvasively using a magnetic resonance imaging method called diffusion tensor imaging (DTI). DTI measures the molecular diffusion of water along neural pathways. Accurate reconstruction of neural connectivity patterns from DTI has been hindered, however, by the inability of DTI to resolve more than a single axon direction within each imaging voxel. Here, we present a novel magnetic resonance imaging technique that can resolve multiple axon directions within a single voxel. The technique, called q-ball imaging, can resolve intravoxel white matter fiber crossing as well as white matter insertions into cortex. The ability of q-ball imaging to resolve complex intravoxel fiber architecture eliminates a key obstacle to mapping neural connectivity in the human brain noninvasively.     

Spatio-Temporal Correlation Tensors Reveal Functional Structure in Human Brain

Resting state functional magnetic resonance imaging (fMRI) has been commonly used to measure functional connectivity between cortical regions, while diffusion tensor imaging (DTI) can be used to characterize structural connectivity of white matter tracts. In principle combining resting state fMRI and DTI data could allow characterization of structure-function relations of distributed neural networks. However, due to differences in the biophysical origins of their signals and in the tissues to which they apply, there has been no direct integration of these techniques to date. We demonstrate that MRI signal variations and power spectra in a resting state are largely comparable between gray matter and white matter, that there are temporal correlations of fMRI signals that persist over long distances within distinct white matter structures, and that neighboring intervoxel correlations of low frequency resting state signals showed distinct anisotropy in many regions. These observations suggest that MRI signal variations from within white matter in a resting state may convey similar information as their corresponding fluctuations of MRI signals in gray matter. We thus derive a local spatio-temporal correlation tensor which captures directional variations of resting-state correlations and which reveals distinct structures in both white and gray matter. This novel concept is illustrated with in vivo experiments in a resting state, which demonstrate the potential of the technique for mapping the functional structure of neural networks and for direct integration of structure-function relations in the human brain.     

Structural and Functional Brain Remodeling during Pregnancy with Diffusion Tensor MRI and Resting-State Functional MRI

Although pregnancy-induced hormonal changes have been shown to alter the brain at the neuronal level, the exact effects of pregnancy on brain at the tissue level remain unclear. In this study, diffusion tensor imaging (DTI) and resting-state functional MRI (rsfMRI) were employed to investigate and document the effects of pregnancy on the structure and function of the brain tissues. Fifteen Sprague-Dawley female rats were longitudinally studied at three days before mating (baseline) and seventeen days after mating (G17). G17 is equivalent to the early stage of the third trimester in humans. Seven age-matched nulliparous female rats served as non-pregnant controls and were scanned at the same time-points. For DTI, diffusivity was found to generally increase in the whole brain during pregnancy, indicating structural changes at microscopic levels that facilitated water molecular movement. Regionally, mean diffusivity increased more pronouncedly in the dorsal hippocampus while fractional anisotropy in the dorsal dentate gyrus increased significantly during pregnancy. For rsfMRI, bilateral functional connectivity in the hippocampus increased significantly during pregnancy. Moreover, fractional anisotropy increase in the dentate gyrus appeared to correlate with the bilateral functional connectivity increase in the hippocampus. These findings revealed tissue structural modifications in the whole brain during pregnancy, and that the hippocampus was structurally and functionally remodeled in a more marked manner.