Summary: Innovative research established a connection between functional network connectivity (FNC) in adolescent brains and cognitive development, including psychiatric risks.
The study analyzed FNC from brain scans of over 9,000 children, finding consistent patterns that serve as individual fingerprints. These patterns can predict a range of behaviors, mental health conditions, and even parental psychopathology influences.
Key Facts:
- Functional connectivity variability in adolescent brain scans carries significant information correlating with cognitive performance and mental health.
- Researchers successfully predicted various conditions from cognitive performance to sleep patterns based on FNC stability across scans.
- A second study developed a brain-wide risk score (BRS) that uses FNC to assess psychiatric risks, showing potential as a biomarker for early intervention.
Source: Georgia State University
Researchers with the Translational Research in Neuroimaging and Data Science (TReNDs) Center at Georgia State have identified important new methods for accurately identifying possible biomarkers in adolescent brains that can reliably predict cognitive developments and psychiatric issues.
A new study, published in Nature Mental Health, represents the first large-scale analysis of its kind in which researchers analyzed functional network connectivity (FNC) across scans and identified associations with a diverse range of health measures in children.
Researchers believe that inferences about early cognitive and psychiatric behaviors in children may be made using these intra-subject variabilities as a useful biomarker.
Researchers studied four scans from more than 9,000 subjects ages 9 to 11.
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 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.
Researchers say that brain functional connectivity derived from functional magnetic resonance imaging (fMRI) is commonly used as a potential blueprint for adults. But they believe that intra-subject variation of FNC can carry biologically meaningful information, especially during adolescence, which is a time of significant change in the brain.
Principal investigator Zening Fu said the study demonstrates that functional connectivity variability can predict a wide range of children’s behavior, including cognition, mental health and sleep conditions.
“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,” said Fu.
“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.”
The research team was able to predict with surprising accuracy a number of conditions or outcomes, including cognitive performance and psychiatric problems.
Researchers were also able to predict sleep conditions and screen usage based on FNC stability. Additionally, they were able to identify brain-behavior correlations with parent psychopathology and prenatal exposure to marijuana and other drugs.
Fu explained how they are able to read the results and, in many cases, predict outcomes in children based on the scans over time.
“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.”
In a second study, published in Biological Psychiatry, research conducted at the TReNDS Center and led by Weizheng Yan finds that functional network connectivity, which steadily reconfigures over time, potentially contains abundant information to assess psychiatric risks. Yan is a former postdoctoral research associate with the TReNDS Center now working with 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 research team discovered that the BRS revealed a distinct, repeatable gradient of FNC patterns for each psychiatric disorder in over 8,000 unaffected teenagers, ranging from low to high risk. The BRS could also identify people with early psychosis from healthy controls and predict psychosis scores.
To generate group-level disorder and healthy control references, researchers used a large brain imaging dataset containing more than 5,000 individuals diagnosed with schizophrenia, autism spectrum, major depressive and bipolar disorders, and their corresponding healthy controls.
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 studies used a multimodal database known as the Adolescent Brain Cognitive Development (ABCD) Study. The dataset contains a wide range of measurements of mental health, cognition and other health-related factors that have been found to be helpful in examining the connection between teenage behaviors and brain function.
The Translational Research in Neuroimaging and Data Science Center (TReNDS) is a collaboration among Georgia State University, the Georgia Institute of Technology and Emory University.
It focuses on developing, applying and sharing advanced analytic approaches and neuroinformatic tools that leverage cutting-edge brain imaging and large-scale data analysis with a goal of translating these approaches into biomarkers that can help address relevant areas of brain health and disease.
About this neurodevelopment and mental health research news
Author: Noelle Reetz
Source: Georgia State University
Contact: Noelle Reetz – Georgia State University
Image: The image is credited to Neuroscience News
Original Research: Open access.
“Functional connectivity uniqueness and variability? Linkages with cognitive and psychiatric problems in children” by Vince Calhoun et al. Nature Mental Health
Closed access.
“A brain-wide risk score for psychiatric disorder evaluated in a large adolescent population reveals increased divergence among higher-risk groups relative to controls” by Vince Calhoun et al. Biological Psychiatry
Abstract
Functional connectivity uniqueness and variability? Linkages with cognitive and psychiatric problems in children
Brain functional connectivity (FC) derived from functional magnetic resonance imaging has been serving as a potential ‘fingerprint’ for adults. However, cross-scan variation of FC can be substantial and carries biological information, especially during childhood.
Here we performed a large-scale cross-sectional analysis on cross-scan FC stability and its associations with a diverse range of health measures in children. Functional network connectivity (FNC) was extracted via a hybrid independent component analysis framework on 9,071 participants and compared across four scans.
We found that FNC can identify a given child from a large group with high accuracy (maximum >94%) and replicated the results across multiple scans. We then performed a linear mixed-effects model to investigate how cross-scan FNC stability was predictive of children’s behaviour.
Although we could not find strong relationships between FNC stability and children’s behaviour, we observed significant but small associations between them (maximum r = 0.1070), with higher stability correlated with better cognitive performance, longer sleep duration and less psychotic expression.
Via a multivariate analysis method, we captured larger effects between FNC stability and children’s cognitive performance (maximum r = 0.2932), which further proved the relevance of FNC stability to neurocognitive development.
Overall, our findings show that a child’s connectivity profile is not only intrinsic but also exhibits reliable variability across scans, regardless of brain growth and development. Cross-scan connectivity stability may serve as a valuable neuroimaging feature to draw inferences on early cognitive and psychiatric behaviours in children.
Abstract
A brain-wide risk score for psychiatric disorder evaluated in a large adolescent population reveals increased divergence among higher-risk groups relative to controls
Background
Accurate psychiatric risk assessment requires biomarkers that are both stable and adaptable to development. Functional network connectivity (FNC), which steadily reconfigures over time, potentially contains abundant information to assess psychiatric risks. However, the absence of suitable analytical methodologies has constrained this area of investigation.
Methods
We investigated 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. To generate group-level disorder and healthy control references, we utilized a large brain imaging dataset containing 5231 total individuals diagnosed with schizophrenia, autism spectrum, major depressive, and bipolar disorders, and their corresponding healthy controls. The BRS metric was employed to assess the psychiatric risk in two new datasets: Adolescent Brain and Cognitive Development (ABCD, N=8191) and Human Connectome Project Early Psychosis (HCP-EP, N=170).
Results
The BRS revealed clear, reproducible gradient of FNC patterns from low-to-high-risk for each psychiatric disorder in unaffected adolescents. We found that low-risk ABCD adolescent FNC patterns for each disorder were strongly present in over 25% of the ABCD subjects and homogeneous, whereas high-risk patterns of each psychiatric disorder were strongly present in about 1% of ABCD subjects and heterogeneous. The BRS also showed its effectiveness in predicting psychosis scores and distinguishing individuals with early psychosis from healthy controls.
Conclusions
The BRS could be a new image-based tool for assessing psychiatric vulnerability over time and in unaffected individuals, and it could also serve as a potential biomarker, facilitating early screening and monitoring interventions.
