High levels of comorbidity in mental disorders represent a core challenge in modern psychiatry. Traditional diagnostic paradigms often struggle to explain the widespread symptom overlap and the developmental transitions between different mental disorders.
While the Hierarchical Taxonomy of Psychopathology (HiTOP) has made strides in explaining behavioral comorbidity (such as the "p-factor"), it has lacked a systematic neurobiological foundation. Conversely, the Research Domain Criteria (RDoC) framework, which focuses on multi-dimensional neurobiological information, lacks a structured neuropathological model to explain why different symptoms are so commonly linked.
To bridge this gap, Assistant Professor Chao Xie’s team at the Department of Psychological and Cognitive Science, Tsinghua University, recently published a groundbreaking study titled "Hierarchical Neurocognitive Model of Externalizing and Internalizing Comorbidity" in the journal Nature Mental Health.
Key Research Findings
Using a 10-year longitudinal neuroimaging-behavioral-genetic cohort spanning from adolescence to young adulthood, the research team identified two distinct classes of hierarchical neural factors at the brain network level:
·Externalizing Symptoms (e.g., impulsivity, aggression, addiction): Characterized by excessive coupling in brain networks related to impulse control and habit systems.
·Internalizing Symptoms (e.g., anxiety, depression, avoidance): Characterized by low coupling in brain networks associated with goal-directed behavior and emotional regulation.
A Unified Hierarchical Framework
Building upon the team’s previous discovery of the NP factor (a general neuro-comorbidity factor representing impaired executive control, published in Nature Medicine, 2023), the researchers proposed a Hierarchical Neurocognitive Model.
This model systematically explains how various psychiatric symptoms converge into stable comorbidity patterns along different functional pathways—moving from high-level executive control down to impulsivity regulation and motivational drives.
The study’s impact includes:
·Robust Validation: Results were validated across multiple independent clinical and population cohorts, including ADHD, Autism Spectrum Disorder (ASD), Depressive Disorder, and Substance Use Disorder.
·Generalizability: The model shows strong predictive power across different ages and diseases.
·Precision Medicine: The identified neural factors correlate specifically with behavioral traits and polygenic risk, providing new targets for risk stratification and precision intervention.
Shifting from "Symptom Classification" to "Mechanism Stratification"
By integrating brain development and cognitive neuroscience, this study fills a critical gap in hierarchical psychopathology theory. It advances the field from a focus on "symptom-based classification" toward "mechanism-based stratification," providing a blueprint for the future of precision diagnosis and treatment in mental health.
About the Authors:
·First Author: Assistant Professor Chao Xie (Tsinghua University).
·Corresponding Author: Professor Tianye Jia (Institute of Drug Dependence, Peking University).
·Key Contributors: Professor Jianfeng Feng and Professor Gunter Schumann (Fudan University).
Full Paper Link: https://www.nature.com/articles/s44220-025-00577-2
Faculty Profile:
Chao Xie
Title: Assistant Professor and Doctoral Supervisor of the Department of Psychological and Cognitive Science
Research Direction: Brain Intelligence Computing and Mental Health
