Brain neuromarkers predict self- and other-related mentalizing across adult, clinical, and developmental samples
Human social interactions rely on the ability to reflect on ones own and others internal states and traits--a process known as mentalizing. Impaired or altered mentalizing is a hallmark of multiple psychiatric and neurodevelopmental conditions. Yet, replicable and easily testable brain markers of mentalizing have so far been lacking. Here, we apply an interpretable machine learning approach to multiple datasets (total N=390) to train and validate fMRI brain signatures that predict i) mentalizing about the self, ii) mentalizing about another person, and iii) both types of mentalizing. Self-mentalizing and other-mentalizing classifiers had positive weights in anterior/medial and posterior/lateral brain areas, respectively, with accuracy rates of 82% and 77% for out-of-sample prediction. The classifier trained across both types of mentalizing showed 98% predictive accuracy and separated (mental) attributional from factual inferences. Classifier patterns revealed better self/other separation in healthy adults compared to individuals with schizophrenia and with increasing age in adolescence. Together, our findings reveal consistent and separable neural patterns subserving trait-based mentalizing about self and others--present at least from the age of adolescence and functionally altered in severe neuropsychiatric disorders. These mentalizing signatures hold promise as candidate neuromarkers of social-cognitive processes in different contexts and clinical conditions. Author NoteThis work was funded by a Starting Grant from the European Research Council (ERC, 101041087) to LKo, a German Academic Exchange Service (DAAD) doctoral grant and a Network of European Neuroscience Schools (NENS) exchange fellowship to DA, an R01 grant from the U.S. National Institutes of Mental Health (R01MH125414-01) to JAH and DAS, a Junior Leader Fellowship from "la Caixa" Foundation (LCF/BQ/PR22/11920017) to PFC, a Consolidator Grant from the European Research Council (ERC, 648082) to LKr, R37, R01 support from the U.S. National Institutes of Mental Health (R37MH076136 to TDW, MH116026 to TDW and L. Chang [PI], R01EB026549 to TDW and M. Lindquist [MPIs]), an NIMH grant (P50MH094258-01A1) to R. Adolphs, and a CIC Brain and Mental Health Chair from the Neurodis foundation to AT. LvdM acknowledges a European Science Foundation EURYI grant (044035001) that funded her doctoral studies (PI: A. Aleman). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. The funders had no role in study design, data analysis, manuscript preparation, or publication decisions.