Advantages of Studying Connectional Neuroanatomy with Diffusion Tractography

下面为大家整理一篇优秀的paper代写范文- Advantages of Studying Connectional Neuroanatomy with Diffusion Tractography，供大家参考学习，这篇论文讨论了用扩散气管造影研究连接神经解剖学的优点。扩散气管造影通过磁共振成像和随后的计算机图像处理技术来研究连接神经生物学。它在反映连接神经解剖学的形式和方向方面具有一定的优势，为该技术的分辨率限制提供了策略。扩散描记法可分为确定性描记法、概率描记法和全局描记法三种类型。每种方法都有一定的优点和局限性。随着技术的发展，现有的问题可以得到解决，从而实现长纤维的示踪。在此基础上，我们相信在扩散气管造影技术创新的基础上，可以得到更可靠的成像结果。

Abstract

Connectional Neuroanatomy investigates the connection tracts and pathways between cerebral cortex. Fasciculi are large pathways formed by white matter fibers. They can be classified into different types according to their roles they, such as commissural, association and projections. Diffusion Tractography investigates the connectional neurobiology through magnetic resonance imaging (MRI) and subsequent computer based image-processing techniques. It has certain adavantages in reflecting the forms and orientations of connectional neuroanatomy and provides tactics to resolution power limit of the technique. Diffusion tractography can be classified into three types, deterministic tractography, probabilistic tractography and global tractography. Each of the method has certain advantages and limitations. With the development of the techniques the existing problems can be solved which lead to the tractography of a long fiber. Thereafter, we are convinced to produce more reliable imaging results based on technological innovation in diffusion tractography.

Connectional Neuroanatomy

Between the cerebral cortex, the lateral ventricle and the deep nucleus, there is a group of fibers connected to the distant regions of the brain. Connectional Neuroanatomy studies the fibers connecting different parts and parcels of the brain, lying between the cortex, the lateral ventricles, and deep nuclei. Fasciculi are large pathways formed by bundles composed of such fibers. White matter fasciculit can be divided into different groups with different names according to their different roles and functions, such as commissural, asscociation and projection tracts. Most of the brain fibers originate and terminate in the cortex, either in the same hemisphere, which are called association fibers (pathways), or in the opposite hemisphere which are termed commissural fibers (pathways). Other fibers connect to the cortex of the subcortical region and they are projective fiber (pathways). The commisural pathways are firbres to connect the two halves of the brain. They play the function of transforming the neuro signals and inputs between two brains. The majority of commisural pathways are composed of anterior commissure, corpus callosum, and the hippocampal commissure, and they are termed after their shapes, origin and termination. The association pathways play the role of connecting different, distant regions between the lobes. The major association pathways include the inferior longitudinal fasciculus, the arcuate fasciculus, the cingulum, the uncinate, and the inferior fronto-occipital fasciculus. There are some fibers, although located in the cerebral hemisphere that only connect the subcortical structures such as the anterior commissure fibers connected to the two amygdalae. Projection pathways include brainstem nuclei, deep cerebral nuclei, and spinal cord. They function to link the cortex to subcortical structures. The descending projection fibers originate from the cortex and terminate in the subcortical nuclei, while ascending projection fibers run the opposite. Most of the projection fibers course through the corona radiata, internal capsule, cerebral peduncles, and brainstem. Notably, there are certain pathways or tracts to contain more than one fiber. Aside from the long tracts that connect human body with the brain, within the cortical and subcortical areas of the brain, there are sophisticated neural networks.

The contact path in the same hemisphere connects to the cortical area and has the front - back (or back - front) direction. The main associated tract was arcuate fiber bundle, cingulate, hook, lower longitudinal fascicle, and lower occipital fasciculus. The term is used to indicate that these domains refer to their shapes (such as hooks, hooks, belts or belts), their origin and termination, such as lower frontal and occipital fasciculus, or their courses and positions (such as inferior longitudinal fasciculus). The association between domain long lobes distant regions, and short U fibers connecting adjacent gyri with leaf (leaf type fiber) or different leaf (leaf fiber). Associated domains are involved in more advanced cognitive functions, such as language, custom, visual spatial processing, memory and emotion.

The way to connect is the fiber of the two hemispheres that are connected to the brain. The main end of the brain of the human brain commissures includes the corpus callosum, the anterior commissure, and the hippocampal commissure. The commissural pathway allows the input of the two halves of the brain and motor and functional integration to play an important role in the transfer of perceptual and cognitive functions.

Diffusion Tractography

Tractography in neuroscience is combination of MRI based on diffusion-weighted images (DWI) with subsequent image processing and analysis technology. The diffusion MRI technique is applied to investigate the mobility of water molecules, through calibrating the average movement as a label for arrangement of tissues. As we know it that, the movement of water is more freely in media without media, isotropic diffusion. While in brain white matters with bundles of axons as barriers, the diffusion is not equal in all direction and thus is termed as “anisotropic” diffusion. Specifically, the water molecule is more inclined to move alone the white matters, on which basis adopting the diffusion MRI technique allows an easy investigation into the structure of the brain. Diffusion Tractography is a method to investigate the aforementioned neurological connections in human brain. It was able to describe the moving situation of fibers. The basic unit diffusion tractography can take is called a voxel, which is around 1-2 mm that far exceeds the diameter of white matter fibers which is around 2 um. However, so long as the white matter firbres forms the bundles, the diffusion tractography can measure the directions. Apparent diffusion coefficient (ADC) map is calculated based upon the diffusion coefficient-related equation and the gradients and water movement are applied to track the diffusion. Given the ADC of water molecules in different tissues, it is not difficult for us to measure the barriers existence and the orientation of fibers, i.e., if we measure along the barriers we obtain higher ADC but if we measure across the barriers we obtain lesser ADC.

Advantages of Using Diffusion Tractography in the Study of Connectional Neuroanatomy

Difficulties in Addressing Connectional Neuroanatomy and Limitations of Traditional MRI technology

To address the advantages of diffusion tractography and relevant methods in investigation the neuroscience based studies, we have to first take into consideration the difficulties of studying these issues. Within the category of connectional neuroanatomy, the unlimited number of connections and the resulting sophisticated neural networks between different cortex and locations make it difficult to study the line and routine in details. More than this, if we adopted the MRI technology itself, our research will be more or less restricted by the limitation of the technique. From an experimental point of view, nuclear magnetic resonance imaging has several important limitations. The elusive visualization before the intrusion may have problems. MRI can dramatically underestimate the growth of diffuse tumor. The evaluation of the treatment response may result from the overlapping pseudo-progress and the changes in the MR signal of the recurrent tumor, which are problematic. It has recognized that the changes in radiotherapy and chemotherapy could be treated as the progress of the tumor. MRI is blind to the location of the WM pathways. The natural physical limitation of MRI resolution technique is considered to be around 10 microns, which is because in typical magnetic resonance time (10-100 milliseconds), water molecules will move so much that we lose track of the signal compared to the extensive noise from the background. As a result, we will lose the sharpness of the technique. Another limitation of this technique is data size. The over-sized image produced from MRI technique can cause troubles to to limit the rate when we try to process the data of the brain tissues we study.

Advantages of the Diffusion-weighted MRI Techniques

As a non-invasive brain imaging technique, diffusion weighted imaging is powerful in tracking the brain white matter fibers in vivo. Diffusion tensor imaging is a kind of specific diffusion-weighted magnetic resonance imaging (DWI) that is used extensively to study and map the brain white matter tractography. In this specific technique, the intensity of different voxel reflected the best estimate of water diffusion rate. DTI plays its role when a tissue such as the nerve fibers in the white matter of the brain or the fibers in the heart fibers, which have some internal fiber structures similar to some crystal anisotropy. Water diffuses faster in the direction of the internal structure, and slows with the movement of the vertical direction. This also means that the diffusivity of the measurement will vary according to the direction observed by the observer. Similarly, diffusion spectrum imaging is another derived technique from DWI which investigates the axon trajectories in a more accurate way for it is sensitive with the changes of the intra-voxel differences in the direction of diffusion that result from crossing fibers. Taking advantage of these techniques and their variations, the arrangement and configuration of white matter tissues can be easily formulated. Considering the above limitation used pure MRI technology, MRI based on water diffusion and contrast is less influenced by the physiological status and is totally dependent by inner brain static situation. Fractional anisotropy (FA) calculates the water molecules’ movement. Anisotropy is found in many body tissues, for instance, kidneys, skeletal and cardiac muscles. However the neural bundles displayed the greatest degree of anisotropy, in which the parallel diffusion is 2–10 times greater when compared to perpendicular diffusion. The anisotrophy based technique hence provide the tools to solve the problems of MRI and improve the rate of processing anatomy data merely on the basis of the tissue configuration and orientation. Compared to the traditional MRI data which may appear the same from different species due to the lack to track on orientation, the DIT generated image solved the problem and provided high contrast images which illustrate the differences well.

Conventional diffusion MRI normally applies three gradient directions to model the diffusion in each voxel as a sphere and detect the amount of water diffusion. Diffusion tractography has its advantage in researching into connectional neuroanatomy in the following aspects. Through tracking and calibrating the diffusive movement within white matter tissues of the brain, we can judge the orientation of those matters. For movement of water molecules in is more freely and without retardation in unrestricted media compared to those in brain white matters, it is easy to figure it out the principle that the movement of molecules alone the bundles formed by the firbres than across those bundles. Therefore, the direction of the bundles can be concluded from the fast diffusion speed. DTI is a variety of diffusion MRI that, by applying six or more gradient directions, models the diffusion in each voxel, and thus can detect both the amount and the directionality of diffusion. The properties of a 3D structure can be defined if we employ the six parameters. Therefore, applying the six parameters from each pixel, we are able to obtain the information of fiber orientation. By converting the information into a color space, one can generate a color coded orientation map. If one can assume the axis orientation and the diffusion tensor, the 3D structure of a certain tissue within the brain we wanted to study can be reconstructed. Beyond this, it is a very useful technique to identify the vascular strokes in the brain. Due to their speciality in identifying anisotrophy of water movement, it is most powerful in neuroanatomy for the tissue of the brain well correlates the situation to promise a single direction water transfer. In such cases, the expansion and retraction of certain tissues can be measured and the images can be compared to yield a convincing result on the current behavior of these diseases, using the apparent diffusion coefficient.

In neuroanatomy, fiber crossing is an important issue that one can not avoid trying to study the track orientation. As mentioned, diffusion tractography of white matter fibers are advantageous in measuring and picturing the orientation of the fiber pathways in a non-invasive means. To visualize a large amount of data obtained in DTI we can adopt different methods as follows. The diffusion tensor graph is easily generated using with three dimensional display capabilities and the result can be displayed as two-dimensional (2D) color. More than this, to achieve the data from DTI to illustrate the fiber orientation of brain white matter, we develop different algorithms. According to the classification of some scholars, there are threes types of fiber tractography algorithms, deterministic, probabilistic and global. Albeit the limitations of each, the different techniques provide different advantages in their applications to solve out the problems lying within the study of white matter structures. I will discuss the limitations and the power of these techniques as follows.

Three Types of Algorithms and Their Limitations And Advantages

Deterministic tractography

Determinsitic or streamline tractography is the most widely applied tractography method. By assuming that the orientation within a voxel is precisely defined, it is able to create a tract or pathway within the brain white matter by directly defining a start point and applying an algorithm correlated voxels with similar diffusion directions. Notably, the technique has the potential to be refined after introducing the criteria of treshold to limit it, such as the deviation angel between different voxels and so on. The accuracy and variance of the tract reconstruction is a function of the algorithm, so as the signal-to-noise ratio, the diffusion tensor eigenvalues, and the tract length. However, there are certain limitations for this algorithm, for instance, crossing white matter tracts create significant challenges for the tract reconstruction.

Probabilistic tractorgraphy

So long as the voxels are much larger compared to the axons, every point within the brain tissue studied can have more than one connections. The aforementioned deterministic method could not certify a point in the white matter tissues without uncertainty. Local fiber direction estimation is affected by error and uncertainty, which will introduce global fiber tracking error. Even a bit of error at a point in the tractography can make the algorithm enter and follow different white matter pathways, leading to a misrepresentation of the white matter connection. To describe this uncertainty, the probability imaging algorithm produces numerous of or distribution results composed of the possible trajectories at each seed point. The higher the density of certain region such as the locus results from the brain region, the higher the density is that can be calculated. The goal of the probabilistic tractography is to acquire the connectivity index along the white matter tracts or pathways. The probability of connecting is paralleled with the seed point. Therefore, the results of the probabilistic streamlines are often quantized. The method explores the number of pathways of a count map traversing each voxel, which can then be easier to analyze and contrast and yield a convincing result.

Global tractography

In addition to the mentioned two types of algorithms, novel algorithms to combine both methods have been developed to find the most perfect explanations of the fibre arrangement and structures based on the current data harvested. Therefore, they do not depend on the fiber orientation extracted from the processing step, but directly manipulate the obtained data to make the process of the imaging phase. These methods rely on a model to predict the intensity of signal in a given fiber direction. Despite the relative high cost in implementing these data, the outcome is promising for it can effectively solve the problems of both deterministic and probabilistic tractorgraphy. With the development of technology, more powerful algorithms of this category can be develop to optimize the outcome and reduce the cost.

So long as there are three algorithms to interpret the data and obtain the final tractorgraphy result, certain pitfalls within the methodology can be resolved, leading to  the anatomical accuracy in the track of the fiber according to its whole length and measuring deficits in the tissues. The study of connectional neuroanatomy has aroused technical problems to demand a lot attention and care paid into the categories to obtain high-resolution Reuters with high fidelity. As time passes, with its extensive applications in different categories of medical sciences, DWI, DTI and relative diffusion tractography techniques are widespread in clinical trials. Evidences have already proved the functionality and practical use of applying this technology in dealing with neurological diagnosis and provide convenience in conducting facial nerve surgeries which allows the avoidance of certain structures. There are three aspects of application for such a technique in connectional neuroanatomy: quantitative comparison of pathways in diseased brain tissues, qualitative comparison of pathways in recovery process, and provide insights by offering anatomical structures. With the rapid development of the high-resolution and high-quality technique, such as ex-vivo DTI, and the increasingly used technology in medical areas, we are confident to foresee a more broad application of such technology in the industry in the future.

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