Adolescent brain biomarkers predict mental health

World-renowned neuroscientist, Distinguished University Professor and head of the TReNDS Center at Georgia State Vince Calhoun worked with the research team to develop the study.

He said the research demonstrates that, independent of brain growth and development, a child’s FNC (functional network connectivity) is robust and stable with high similarity across scans and can serve as a fingerprint to identify an individual child from a large group.

“This study is quite exciting as it shows the promise of using advanced machine learning to identify brain patterns which might help us intervene early in children who are most at risk for cognitive or psychiatric problems,” said Calhoun, who is the senior author of the study.
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“FNC stability in our present work is defined as the variability or changes in the resting-state functional connectivity across scans (measurements),” Fu said.

“That is, if a subject has been collected using resting-state fMRI scans multiple times, the functional connectivity estimated using each fMRI scan should be different, even if they are from the same subject. Such difference or variability is not trivial, but biologically meaningful. Subjects with larger FNC variability (smaller stability) might tend to have lower cognitive performance and more mental health problems.”