[Editors] MIT model helps researchers 'see' brain development

[Elizabeth Thomson]

MIT News Office
Massachusetts Institute of Technology
Room 11-400
77 Massachusetts Avenue
Cambridge, MA  02139-4307
Phone: 617-253-2700
http://web.mit.edu/newsoffice/www

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MIT model helps researchers 'see' brain development

--Work could facilitate early detection of autism
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For Immediate Release
MONDAY, APR. 9, 2007
Contact: Elizabeth A. Thomson, MIT News Office
Phone: 617-258-5402
Email: thomson at mit.edu

IMAGES AVAILABLE

CAMBRIDGE, Mass.--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.

In addition to Yu, Grant and Fischl, co-authors on the paper are 
postdoctoral associate Yuan Qi and Assistant Professor Polina Golland 
of CSAIL (Golland also holds an appointment in MIT's Department of 
Electrical Engineering and Computer Science); Xiao Han of CMS Inc.; 
Florent Segonne of Certis Laboratory; Rudolph Pienaar, Evelina Busa, 
Jenni Pacheco and Nikos Makris of the Martinos Center; and Randy L. 
Buckner of Harvard University and the Martinos Center.

The research was supported by the National Center for Research 
Resources, the National Institutes of Health, the Washington 
University Alzheimer's Disease Research Center, and the Mental 
Illness and Neuroscience Discovery (MIND) Institute. It is part of 
the National Alliance for Medical Image Computing, funded by the 
National Institutes of Health.

--END--

Written by Elizabeth Dougherty, Harvard-MIT Division of Health 
Sciences and Technology