Summary: A groundbreaking study reveals how a large dataset was used to uncover brain imaging biomarkers that can predict mental illness in adolescents. The researchers utilized resting-state functional connectivity analysis to identify a brain connectivity pattern that is linked to cognitive function and psychopathological symptoms. This pattern not only indicated current psychiatric disorders but also predicted the onset and progression of various mental health issues over two years. The findings could potentially revolutionize the diagnosis and treatment of psychiatric conditions, leading to more objective, brain-based assessments.
Key Facts:
- The study analyzed brain imaging data from almost 12,000 children and identified predictive biomarkers for mental illness.
- The identified brain connectivity pattern was positively correlated with cognitive function and negatively with psychopathology.
- These biomarkers successfully predicted both current and future psychiatric disorders, pointing to a new direction for adolescent mental health diagnosis.
Source: Elsevier
Research and treatment of psychiatric disorders are hindered by a lack of biomarkers – objective biological or physiological markers that can assist in diagnosing, tracking, predicting, and treating diseases.
In a recent study published by Elsevier’s Biological Psychiatry, researchers used a large dataset to identify predictive brain imaging-based biomarkers for mental illness in adolescents. Traditionally, psychiatric disorders like depression have been diagnosed based on subjective assessments. The identification of biomarkers for diagnosis and treatment selection could greatly advance treatments.
In this study, the researchers utilized brain imaging data from the Adolescent Brain Cognitive Development (ABCD) Study, involving nearly 12,000 children aged 9 to 10 at the beginning of the study. Advanced neuroimaging techniques, including resting-state functional connectivity (rsFC) analysis, enable researchers to explore the organization of brain circuits and their interaction over time. Dr. Yihong Yang, the senior author of the study, shared, “Using a functional MRI dataset, we identified a brain connectivity pattern that is positively correlated with cognitive functions and negatively correlated with psychopathological measures.”
The brain-based connectivity pattern predicted the number of psychiatric disorders in participants during the scan as well as over the following two years, demonstrating how it can forecast the transition of diagnosis across disorders. Dr. Yang added, “These findings provide evidence for a transdiagnostic brain-based measure that underlies individual differences in developing psychiatric disorders in early adolescence.”
A highlighting aspect of this study is the potential of using neuroimaging data to illuminate risk for mental illness across a range of diagnoses. Dr. Krystal, Editor of Biological Psychiatry, noted, “Mental illness in adolescence has emerged as a cardinal public health challenge in the post-COVID era. More than ever before, we would benefit from better ways to identify adolescents at risk.”
Overall, by finding biomarkers of mental illnesses, the study contributes to aligning psychiatric diagnosis with other medical diagnoses, potentially providing a more precise means of diagnosis, rather than relying solely on symptoms.
About this neuroimaging and mental health research news
Author: Eileen Leahy
Source: Elsevier
Contact: Eileen Leahy – Elsevier
Image: The image is credited to Neuroscience News
Original Research: Open access.
“Brain Functional Connectome Defines a Transdiagnostic Dimension Shared by Cognitive Function and Psychopathology in Preadolescents” by Yihong Yang et al. Biological Psychiatry
Abstract
Brain Functional Connectome Defines a Transdiagnostic Dimension Shared by Cognitive Function and Psychopathology in Preadolescents
Background
Cognitive function and general psychopathology are two important classes of human behavior dimensions, individually related to mental disorders across diagnostic categories. However, whether the two transdiagnostic dimensions link to common or distinct brain networks that convey resilience or risk for the development of psychiatric disorders remains unclear.
Method
The current study is a longitudinal investigation with 11,875 youths aged 9- to 10-years-old at study onset, from the Adolescent Brain Cognitive Development study. A machine-learning approach based on canonical correlation analysis was used to identify latent dimensional associations of the resting-state functional connectome with multi-domain behavioral assessments including cognitive functions and psychopathological measures. For the latent rsFC factor showing a robust behavioral association, its ability to predict psychiatric disorders was assessed using two-year follow-up data and its genetic association was evaluated using twin data from the same cohort.
Results
A latent functional connectome pattern was identified that showed a strong and generalizable association with the multi-domain behavioral assessments (5-fold cross validation: ρ = 0.68∼0.73, for the training set (N = 5096); ρ = 0.56 ∼ 0.58, for the test set (N = 1476)). This functional connectome pattern was highly heritable (h2 = 74.42%, 95% CI: 56.76%-85.42%), exhibited a dose-response relationship with the cumulative number of psychiatric disorders assessed concurrently and 2-years post-MRI-scan, and predicted the transition of diagnosis across disorders over the 2-year follow-up period.
Conclusion
These findings provide preliminary evidence for a transdiagnostic connectome-based measure that underlies individual differences in developing psychiatric disorders in early adolescence.
