Researchers describe new methods for precisely locating potential biomarkers in teenage brains that are capable of accurately forecasting cognitive growth and mental health problems. Their new study is the first large-scale analysis of its kind, in which researchers analyzed functional network connectivity (FNC) across scans and identified associations with a variety of health measures in children.
Researchers believe that by using intra-subject variability as a biomarker, they can make inferences about early cognitive and psychiatric behaviors in children.
“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 senior author Vince Calhoun.
Functional Connectivity Variability
Calhoun, head of the Translational Research in Neuroimaging and Data Science (TReNDs) Center at Georgia State University, worked with the research team to develop the study. According to him, the research shows that, independent of brain growth and development, a child’s FNC is robust and stable, with high similarity across scans, and can serve as a fingerprint to identify an individual child from a large group.
Researchers studied four scans from more than 9,000 subjects ages 9 to 11. According to the study’s principal investigator, Zening Fu, functional connectivity variability can predict a wide range of children’s behavior, including cognition, mental health, and sleep patterns.
“Most previous fMRI studies believe that resting-state functional connectivity can provide a fingerprint of an individual, and that variability in connectivity is due to noise or other confounding effects. However, we found that the variations of individualized FNC across scans are notable and convey psychological and physiological information underlying distinct behavioral phenotypes in children. Multivariate methods could help to capture much larger effects between FNC stability and children’s behavior,”
said Fu.
Brain-behavior Links
The research team was able to predict with surprising accuracy a number of conditions or outcomes, including cognitive performance and psychiatric problems. Based on FNC stability, researchers were also able to predict sleep conditions and screen usage. They were also able to uncover brain-behavior links with parent psychopathology and prenatal marijuana and other drug exposure.
Fu explains how they can read the results and, in many cases, predict outcomes in children based on scans taken over time.
“FNC stability in our present work is defined as the variability or changes in the resting-state functional connectivity across scans (measurements). 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.”
Fu said.
Brain-wide Risk Score
A second study led by Weizheng Yan at the TReNDS Center reveals that functional network connectivity, which constantly reconfigures over time, may hold a wealth of information for assessing psychiatric risks. Yan is a former TReNDS Center postdoctoral research associate who now works for the National Institutes of Health.
As part of the study, researchers developed a brain-wide risk score (BRS), a novel FNC-based metric that contrasts the relative distances of an individual’s FNC to that of psychiatric disorders versus healthy control references.
The BRS indicated a distinct, repeatable gradient of FNC patterns for each psychiatric disorder in over 8,000 unaffected youths ranging from low to high risk, according to the research team. The BRS could also distinguish between persons with early psychosis and healthy controls, as well as predict psychosis scores.
The researchers used a large brain imaging dataset containing over 5,000 individuals diagnosed with schizophrenia, autism spectrum disorder, major depressive disorder, and bipolar disorder, as well as their corresponding healthy controls, to generate group-level disorder and healthy control references.
Assessing Psychiatric Vulnerability
The findings show that the BRS could be a new image-based tool for assessing psychiatric vulnerability over time and in unaffected individuals, and could also serve as a potential biomarker, facilitating early screening and monitoring interventions.
Both investigations made use of the Adolescent Brain Cognitive Development (ABCD) Study, a multimodal database. There are many tests in the dataset that look at mental health, cognitive function, and other health-related factors. These tests have been shown to help researchers find links between teen behavior and brain function.
TReNDS is a collaboration between Georgia State University, Georgia Institute of Technology, and Emory University.
References:
- Fu, Z., Liu, J., Salman, M.S. et al. Functional connectivity uniqueness and variability? Linkages with cognitive and psychiatric problems in children. Nat. Mental Health (2023). doi: 10.1038/s44220-023-00151-8
- Yan, Weizheng et al. A brain-wide risk score for psychiatric disorder evaluated in a large adolescent population reveals increased divergence among higher-risk groups relative to controls. Biological psychiatry, S0006-3223(23)01592-5. 26 Sep. 2023, doi:10.1016/j.biopsych.2023.09.017