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.