包括人类、猿类或是属于小型动物的猫、鼠等的哺乳动物，其大脑的外型，都呈现多皱折的结构，因此过去就有科学家提出这样的看法，认为这些皱折形成的方式，很可能和大脑发育的过程有直接的关系，但毕竟存在于大脑的未解迷团仍然非常的多，使得这样的一个说法，始终是一个未曾解开的迷团。
四月份最新一期 IEEE期刊的一篇论文指出，由美国麻省理工学院哈佛大学医学院和麻州综合医院 (Massachusetts General Hospital)的科学家所组成的研究团队，成功的利用核磁共振技术 (magnetic resonance；简称 MR)，搭配先进的计算机影像技术，就尝试着利用不同时期的大脑皮层(cerebral cortex) 皱折影像，去了解皱折跟发育可能存在的关系。
研究人员募集了 11名个体，其中包括8 位30 至40周孕期所出生的婴儿，另外 3名两岁、三岁以及七岁的幼童，透过 MR这种非侵入性的方式，仔细的扫瞄大脑皮质皱折最多的区域，科学家想借着这样的观察，了解发育与皱折之间的关系，同时观察皱折异常可能存在的神经组织病变的机会。
结果研究人员确实利用这种 MR技术，搭配高解析影像分析，获得比过去还要清析的大脑皮质皱折影像，因此下一步就是长期的追踪这些受检的样品，并同时比对那些发生病变的脑组织，看看可不可以建立皱折与脑发育的关系。
(资料来源 : Bio.com)
英文原文: 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).
[NextPage]
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
[NextPage]
