Study: MRI can assess brain maturity, developmental disorders
Functional connectivity MRI can reliably document changes in human functional brain maturity across development, and potentially highlight psychiatric and developmental disorders, according to a study published online Sept. 10 in Science.
Nico U. F. Dosenbach, MD, PhD, from the department of neurology at Washington University School of Medicine in St. Louis, and colleagues compared functional data to standardized models of how brain function or disease normally develops in order to determine which regions work together in brain networks as the brain matures.
Just as differences in brain region shapes can be identified via MRI, brain imaging “also offers ways to analyze how different parts of the brain work together functionally," the authors offered.
The researchers recruited 238 typically developing volunteers between the ages of 7 to 30 years for their study. Each participant underwent five-minute resting state MRI scans, and a support vector machine was utilized in order to analyze approximately 13,000 functional brain connections and select the best 200 to produce a single index of the maturity of each subject. The index helped distinguish the child volunteers from the adults within the study population, and approximated a curving line to track the path of normal functional brain development, the authors explained.
Dosenbach and colleagues predicted that patients with brain disorders who may not be presenting symptoms would appear out of alignment with this normal developmental curve.
The resultant functional maturation curve accounted for 55 percent of the sample variance and followed a nonlinear asymptomatic growth curve shape, determined the authors. The greatest relative contribution to predicting individual brain maturity was made by the weakening of short-range functional connections between the adult brain’s major functional networks.
“We show that support vector machine-based multivariate pattern analysis extracts sufficient information from functional connectivity MRI data to make accurate predictions about individuals’ brain maturity across development,” Dosenbach and colleagues wrote.
With the possibly of identifying disorders and administering treatment prior to the onset of symptoms, physicians may be better able to track the results of clinical trials of new therapies, the authors said.
While Dosenbach noted that MRI scanning is costly, “many children with these types of disorders already receive regular structural MRI scans, and five more minutes in the scanner won't add that much to the cost," he concluded.
Nico U. F. Dosenbach, MD, PhD, from the department of neurology at Washington University School of Medicine in St. Louis, and colleagues compared functional data to standardized models of how brain function or disease normally develops in order to determine which regions work together in brain networks as the brain matures.
Just as differences in brain region shapes can be identified via MRI, brain imaging “also offers ways to analyze how different parts of the brain work together functionally," the authors offered.
The researchers recruited 238 typically developing volunteers between the ages of 7 to 30 years for their study. Each participant underwent five-minute resting state MRI scans, and a support vector machine was utilized in order to analyze approximately 13,000 functional brain connections and select the best 200 to produce a single index of the maturity of each subject. The index helped distinguish the child volunteers from the adults within the study population, and approximated a curving line to track the path of normal functional brain development, the authors explained.
Dosenbach and colleagues predicted that patients with brain disorders who may not be presenting symptoms would appear out of alignment with this normal developmental curve.
The resultant functional maturation curve accounted for 55 percent of the sample variance and followed a nonlinear asymptomatic growth curve shape, determined the authors. The greatest relative contribution to predicting individual brain maturity was made by the weakening of short-range functional connections between the adult brain’s major functional networks.
“We show that support vector machine-based multivariate pattern analysis extracts sufficient information from functional connectivity MRI data to make accurate predictions about individuals’ brain maturity across development,” Dosenbach and colleagues wrote.
With the possibly of identifying disorders and administering treatment prior to the onset of symptoms, physicians may be better able to track the results of clinical trials of new therapies, the authors said.
While Dosenbach noted that MRI scanning is costly, “many children with these types of disorders already receive regular structural MRI scans, and five more minutes in the scanner won't add that much to the cost," he concluded.