生物谷报道：一些大的哺乳动物如人、猴、甚至猫的脑都有一个神秘的特性：即其最外层都是折叠表面。这些折叠的功能意义是神经科学领域悬而未决的一个大问题。如今，由麻萨诸塞州总医院和哈佛医学院研究者带领的一个研究小组已经建立了一个可以帮助研究者“看到”大脑皮层的这些折叠是如何发育及衰退的工具。他们将计算机成像技术应用至通过磁共振成像收集到的脑的影像，创建了一系列工具来全程示踪并测量这些折叠，所得到的皮层发育的运算结果模型或许可以作为生物标记或生物指示剂应用于象孤独症的神经障碍的早期诊断。
研究者在四月份的美国电机及电子工程师学会会刊医学版上对他们的模型进行了描述和分析。哈佛-麻省理工学院健康科学技术专业的毕业生Peng Yu是这篇文章的第一作者。研究工作是由哈佛医学院的影像学副教授、本文的合著者Bruce Fischl，麻省理工学院计算机科学和智能化实验室的研究成员，以及麻萨诸塞州综合医院Martinos 生物医学影像中心的计算核心指导者共同带领进行的。研究小组收集了MGH 和Marinos中心儿科放射学权威Ellen Grant提供的11例发育中脑的MR影像。接受扫描者中，8例是大约在妊娠30-40周时已基本成熟的新生儿，其他3例分别是2、3、7岁的儿童。Grant通过扫描这些婴儿和儿童的脑来评估其可能的脑损伤，结果发现没有神经缺损。随后，她与Fischl小组进行了商讨以确保他们分析具有临床意义。Yu说，我们不能打开脑用肉眼来观察，但是我们可以通过MR来研究，而且这项技术比早期的X线显象安全的多。对这些影像进行分析的第一步工作就是对他们的共同解剖学结构进行校准，例如将运动皮层与躯体感觉皮层分离开的皱襞――中央沟。Yu利用Fischl建立的一种技术来进行这种校准。第二步工作是建立一种脑皱襞的数字化模型，使得研究者可以对其改变进行全程、全方位的分析。最初的脑扫描是以位点来进行描绘的。记录一个婴儿脑的一侧大脑半球大约需要130,000个位点。Yu将这些点分解成为只需42个位点的仅仅显示粗糙皱襞的图表。通过加入更多的点，她得到了区域越来越精细而分辨率越来越高的皱襞。最后，Yu利用Grant推荐的技术建立了生物生长模型，这项技术可以使她根据皱襞的类型、精细程度、发达程度等迅速对年龄作出鉴定。她发现相当于一张皱纸的最大皱褶的粗糙皱襞比细粒皱襞发育早而慢。
除了提供对皮层发育的观察能力外，该研究小组正将这些影像与那些有孤独症的患者资料进行比较。Fischl说:“我们现在关于正常发育已经有了一定的认识，下一步要通过观察皱襞的差异来检测象孤独症这样的疾病中的异常发育”。这个工具也可以用于检测其他神经疾病如精神分裂症和阿尔茨海默病。
原文出处：
MIT Model Helps Researchers 'See' Brain Development
04/09/07 -- Large mammals--humans, monkeys, and even cats--have brains with a somewhat mysterious feature: The outermost layer has a folded surface. Understanding the functional significance of these folds is one of the big open questions in neuroscience.
Now a team led by MIT, Massachusetts General Hospital and Harvard Medical School researchers has developed a tool that could aid such studies by helping researchers ?see? how those folds develop and decay in the cerebral cortex.
By applying computer graphics techniques to brain images collected using magnetic resonance (MR) imaging, they have created a set of tools for tracking and measuring these folds over time. Their resulting model of cortical development may serve as a biomarker, or biological indicator, for early diagnosis of neurological disorders such as autism.
The researchers describe their model and analysis in the April issue of IEEE Transactions on Medical Imaging.
Peng Yu, a graduate student in the Harvard-MIT Division of Health Sciences and Technology (HST), is first author on the paper. The work was led by co-author Bruce Fischl, associate professor of radiology at Harvard Medical School, research affiliate with the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and HST, and director of the computational core at the HST Martinos Center for Biomedical Imaging at Massachusetts General Hospital (MGH).
The team started with a collection of MR images from 11 developing brains, provided by Ellen Grant, chief of pediatric radiology at MGH and the Martinos Center. Of the subjects scanned, eight were newborn, mostly premature babies ranging from about 30 to 40 weeks of gestational age, and three were from children aged two, three and seven years. Grant scanned these infants and children to assess possible brain injury and found no neural defects. Later, she also consulted with Fischl's team to ensure that their analyses made sense clinically.
?We can't open the brain and see by eye, but the cool thing we can do now is see through the MR machine,? a technology that is much safer than earlier techniques such as X-ray imaging, said Yu.
The first step in analyzing these images is to align their common anatomical structures, such as the ?central sulcus,? a fold that separates the motor cortex from the somatosensory cortex. Yu applied a technique developed by Fischl to perform this alignment.
The second step involves modeling the folds of the brain mathematically in a way that allows the researchers to analyze their changes over time and space.
The original brain scan is then represented computationally with points. Charting each baby's brain requires about 130,000 points per hemisphere. Yu decomposed these points into a representation using just 42 points that shows only the coarsest folds. By adding more points, she created increasingly finer-grained domains of smaller, higher-resolution folds.
Finally, Yu modeled biological growth using a technique recommended by Grant that allowed her to identify the age at which each type of fold, coarse or fine, developed, and how quickly.
She found that the coarse folds, equivalent to the largest folds in a crumpled piece of paper, develop earlier and more slowly than fine-grained folds.
In addition to providing insights into cortical development, the team is now comparing the images to those being collected from patients with autism. ?We now have some idea of what normal development looks like. The next step is to see if we can detect abnormal development in diseases like autism by looking at folding differences,? said Fischl. This tool may also be used to shed light on other neurological diseases such as schizophrenia and Alzheimer's disease.
Source: Massachusetts Institute of Technology
http://www.bio.com/newsfeatures/newsfeatures_research.jhtml?cid=28200002
作者简介：
Bruce Fischl
B.A., Mathematics, Wesleyan University, Middletown CT
Ph.D., Cognitive and Neural Systems, Boston University, Boston MA
Curriculum Vitae
Publications
Research Interests:
Magnetic Resonance Imaging Computer/Robot Vision Nonlinear image enhancement Nonlinear anisotropic diffusion Autonomous Mobile Robot Navigation Dynamic Receptive Fields
Research related links
Cortical Surface reconstruction and analysis projects at the MGH NMR Center MPEG movies of brain unfolding, flattening and activity on Marty Sereno's home page at UCSD Stanford Vision and Imaging Science and Technology Boston University's Computational Vision and Robotics Group UMass Robotics Internet Resources Page Computer Vision jump page Carnegie Mellon NavLab Home Page Robotics Jump Page The Reinforcement Learning Group at Carnegie Mellon The Artificial Intelligence Laboratory at MIT Luciano da F. Costa's review of real-time imaging and vision sites The Evolution of Mean Curvature in Image Filtering
