Scaling up: Brain-based psychometric assessments target functional networks across psychiatric disorders

The global burden of psychiatric disorders is rising ^1,2 and there is an urgent need for more effective systems of diagnosis, prediction, and treatment. While advances in neuroimaging have accelerated the discovery of brain networks involved across psychiatric conditions, whole-brain imaging techniques face limited clinical practicality due to high costs and restricted availability. The development of self-report psychometric tests targeting distinct functional networks in the brain may enable the affordable, rapid, and scalable measurement of psychiatric biomarkers without the need for imaging. Here, we show that individual self-report prompts (“items”) can be organized into distinct scales targeting functional brain networks, serving as biomarkers for brain connectivity across psychiatric conditions. We used the YaleNeuroConnect dataset ³ , comprised of a transdiagnostic cohort of participants (n=302) who completed a comprehensive psychometric battery with 318 total assessment items and underwent eight runs of resting and task-based functional magnetic resonance imaging (fMRI). Functional connectivity matrices were generated for each participant and partitioned into ten distinct canonical brain networks. Kernel ridge regression models were then trained on participant network connectivity and used to predict their scores on individual psychometric items. Overall, whole-brain functional connectivity significantly predicted 79% of psychometric items (FDR < 0.01) with every item being predicted by at least one functional network (FDR < 0.01). Each functional network was strongly linked to a unique group of psychometric items that could be aggregated into a single network score with a strong predictive basis in the brain. Next, a sequential feature selection paradigm was employed to identify combinations of items that optimally estimated participant connectivity across networks. Network-specific item weights extracted from these predictive models were able to significantly predict network connectivity and formed scales that recapitulated connectivity profiles across psychiatric diagnoses and general psychopathology. Using the functional network-basis for each item, we then developed and administered a brain-based scale, termed the “Functional Network Connectivity Index - Brief” (FNCI-B), to an external cohort (n=600) to evaluate the psychometric validity of this neurobiologically-driven development process. Administering the FNCI-B while varying properties such as item composition and order preserved critical psychometric properties such as factor structure. Together, these results indicate that brain-based psychometric scales can be developed with fMRI data to target specific functional networks – laying the foundation for the implementation of neural biomarkers in the clinic and advancing systems of diagnosis and treatment in psychiatry.