生物谷報(bào)道：一些大的哺乳動(dòng)物如人、猴、甚至貓的腦都有一個(gè)神秘的特性：即其最外層都是折疊表面,。這些折疊的功能意義是神經(jīng)科學(xué)領(lǐng)域懸而未決的一個(gè)大問題,。如今，由麻薩諸塞州總醫(yī)院和哈佛醫(yī)學(xué)院研究者帶領(lǐng)的一個(gè)研究小組已經(jīng)建立了一個(gè)可以幫助研究者“看到”大腦皮層的這些折疊是如何發(fā)育及衰退的工具,。他們將計(jì)算機(jī)成像技術(shù)應(yīng)用至通過磁共振成像收集到的腦的影像,，創(chuàng)建了一系列工具來全程示蹤并測(cè)量這些折疊，所得到的皮層發(fā)育的運(yùn)算結(jié)果模型或許可以作為生物標(biāo)記或生物指示劑應(yīng)用于象孤獨(dú)癥的神經(jīng)障礙的早期診斷,。
研究者在四月份的美國(guó)電機(jī)及電子工程師學(xué)會(huì)會(huì)刊醫(yī)學(xué)版上對(duì)他們的模型進(jìn)行了描述和分析,。哈佛-麻省理工學(xué)院健康科學(xué)技術(shù)專業(yè)的畢業(yè)生Peng Yu是這篇文章的第一作者。研究工作是由哈佛醫(yī)學(xué)院的影像學(xué)副教授,、本文的合著者Bruce Fischl,，麻省理工學(xué)院計(jì)算機(jī)科學(xué)和智能化實(shí)驗(yàn)室的研究成員，以及麻薩諸塞州綜合醫(yī)院Martinos 生物醫(yī)學(xué)影像中心的計(jì)算核心指導(dǎo)者共同帶領(lǐng)進(jìn)行的,。研究小組收集了MGH 和Marinos中心兒科放射學(xué)權(quán)威Ellen Grant提供的11例發(fā)育中腦的MR影像,。接受掃描者中,，8例是大約在妊娠30-40周時(shí)已基本成熟的新生兒,，其他3例分別是2、3,、7歲的兒童,。Grant通過掃描這些嬰兒和兒童的腦來評(píng)估其可能的腦損傷，結(jié)果發(fā)現(xiàn)沒有神經(jīng)缺損,。隨后,，她與Fischl小組進(jìn)行了商討以確保他們分析具有臨床意義。Yu說,，我們不能打開腦用肉眼來觀察,，但是我們可以通過MR來研究，而且這項(xiàng)技術(shù)比早期的X線顯象安全的多,。對(duì)這些影像進(jìn)行分析的第一步工作就是對(duì)他們的共同解剖學(xué)結(jié)構(gòu)進(jìn)行校準(zhǔn),，例如將運(yùn)動(dòng)皮層與軀體感覺皮層分離開的皺襞――中央溝。Yu利用Fischl建立的一種技術(shù)來進(jìn)行這種校準(zhǔn),。第二步工作是建立一種腦皺襞的數(shù)字化模型,，使得研究者可以對(duì)其改變進(jìn)行全程、全方位的分析,。最初的腦掃描是以位點(diǎn)來進(jìn)行描繪的,。記錄一個(gè)嬰兒腦的一側(cè)大腦半球大約需要130,000個(gè)位點(diǎn)。Yu將這些點(diǎn)分解成為只需42個(gè)位點(diǎn)的僅僅顯示粗糙皺襞的圖表,。通過加入更多的點(diǎn),，她得到了區(qū)域越來越精細(xì)而分辨率越來越高的皺襞。最后,，Yu利用Grant推薦的技術(shù)建立了生物生長(zhǎng)模型,，這項(xiàng)技術(shù)可以使她根據(jù)皺襞的類型,、精細(xì)程度、發(fā)達(dá)程度等迅速對(duì)年齡作出鑒定,。她發(fā)現(xiàn)相當(dāng)于一張皺紙的最大皺褶的粗糙皺襞比細(xì)粒皺襞發(fā)育早而慢,。
除了提供對(duì)皮層發(fā)育的觀察能力外，該研究小組正將這些影像與那些有孤獨(dú)癥的患者資料進(jìn)行比較,。Fischl說:“我們現(xiàn)在關(guān)于正常發(fā)育已經(jīng)有了一定的認(rèn)識(shí),，下一步要通過觀察皺襞的差異來檢測(cè)象孤獨(dú)癥這樣的疾病中的異常發(fā)育”。這個(gè)工具也可以用于檢測(cè)其他神經(jīng)疾病如精神分裂癥和阿爾茨海默病,。
原文出處：
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
作者簡(jiǎn)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
