Connectome-based modeling of the suicidal brain: neurobiological substrates, clinical translation and future perspectives.

Abstract

Suicide represents a global public health crisis, claiming over 700,000 lives worldwide every year. Deep understanding of the neurobiological mechanisms will enable objective risk assessment and more effective prevention of suicide. Recent advances in neuroimaging have revealed that suicidal thoughts and behaviors are associated with disruptions in both structural and functional brain connectome organization. In addition, brain connectome profiles may represent "fingerprints" that capture individual variability in suicidality, offering a transformative framework for personalized assessment. In this review, we discuss the latest developments in connectome-based modeling of the suicidal brain, which can not only inform neurobiological mechanisms but also help to advance clinical translation. We first summarize previous MRI-based connectomic findings, emphasizing convergent evidence for disrupted prefrontal-limbic-subcortical circuitry and reduced global network integration as core features of suicidality. Next, we review PET and EEG/MEG studies to highlight future directions for multimodal and integrative connectomic research. With a specific focus on individualized application, we also highlight connectome-based machine learning findings and propose a novel paradigm using normative models to develop a connectome-based suicide risk calculator. Finally, we present current challenges and future directions to improve brain connectome research in suicidality, emphasizing the imperative of using high-quality longitudinal cohorts for validation.