A new technique reveals itAll the so-called intelligent mammals, from cats and elephants to monkeys, dolphins and humans present a highly folded surface of the brain.
The functional meaning of these folds is a huge puzzle in neuroscience.
A team from MIT, Massachusetts General Hospital and Harvard Medical School has found a method to detect how the brain's cortex folds develop and decay based on computer graphics techniques employing brain images achieved through magnetic resonance (MR) imaging.
The new model of cortical development could be employed for early detection of neurological disorders such as autism, schizophrenia and Alzheimer's disease. The team used MR images coming from 11 developing brains with no neural defects.
8 subjects were newborns, mostly premature babies at 30 to 40 weeks of gestational age, and three were from children aged 2, 3 and 7. "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 first author Peng Yu, a graduate student in the Harvard-MIT Division of Health Sciences and Technology (HST).
First, the researchers aligned the common anatomical structures of the brains, like the "central sulcus," the borderline between the motor cortex and the sensory cortex. 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 brain scan were figured computationally with about 130,000 points per hemisphere. Yu simplified this representation to only 42 points tracking just the coarsest folds.
More points followed the development of increasingly smaller folds. The team could detect the age at which each type of fold, coarse or fine, emerged and how quickly it developed.
The coarse folds were the first to appear but their development was slower. "The next step is to see if we can detect abnormal development in diseases like autism by looking at folding differences," said 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).