包括人類,、猿類或是屬于小型動物的貓、鼠等的哺乳動物,，其大腦的外型,，都呈現(xiàn)多皺折的結(jié)構(gòu)，因此過去就有科學(xué)家提出這樣的看法,，認為這些皺折形成的方式,，很可能和大腦發(fā)育的過程有直接的關(guān)系，但畢竟存在于大腦的未解迷團仍然非常的多,，使得這樣的一個說法，始終是一個未曾解開的迷團,。
四月份最新一期 IEEE期刊的一篇論文指出,，由美國麻省理工學(xué)院哈佛大學(xué)醫(yī)學(xué)院和麻州綜合醫(yī)院 (Massachusetts General Hospital)的科學(xué)家所組成的研究團隊，成功的利用核磁共振技術(shù) (magnetic resonance,；簡稱 MR),，搭配先進的計算機影像技術(shù)，就嘗試著利用不同時期的大腦皮層(cerebral cortex) 皺折影像,，去了解皺折跟發(fā)育可能存在的關(guān)系,。
研究人員募集了 11名個體，其中包括8 位30 至40周孕期所出生的嬰兒,，另外 3名兩歲,、三歲以及七歲的幼童，透過 MR這種非侵入性的方式,，仔細的掃瞄大腦皮質(zhì)皺折最多的區(qū)域,，科學(xué)家想借著這樣的觀察，了解發(fā)育與皺折之間的關(guān)系,，同時觀察皺折異?？赡艽嬖诘纳窠?jīng)組織病變的機會。
結(jié)果研究人員確實利用這種 MR技術(shù),，搭配高解析影像分析,，獲得比過去還要清析的大腦皮質(zhì)皺折影像，因此下一步就是長期的追蹤這些受檢的樣品,，并同時比對那些發(fā)生病變的腦組織,，看看可不可以建立皺折與腦發(fā)育的關(guān)系。
(資料來源 : 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).
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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
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