The transdiagnostic importance of brain networks identified through network mapping

Reliable neural biomarkers for psychiatric disorders including Major Depressive Disorder (MDD) have remained difficult to define, partially due to the variability in brain alterations. In this talk, I will discuss how network mapping analyses can help bridge this gap. This approach considers whether apparently isolated brain changes are actually linked in terms of distributed brain networks. While studies using normative connectomes have shown depression-related alterations converge on specific neural circuits, transdiagnostic validation in clinical populations has yet to be explored. We examined depression networks using diffusion-weighted and resting-state fMRI data from a large cohort (N=2592), including individuals with MDD, bipolar disorder (BD), schizophrenia (SZ), and healthy controls (HC). By analyzing brain connectivity patterns, we aimed to determine their relationship with psychiatric symptoms, cognitive deficits, genetic risks, and early-life stress. Using regression models and machine learning, we explored whether connectivity differences can distinguish diagnostic groups and reveal shared neurobiological features. This work advances our understanding of depression’s neural signatures by integrating multimodal data and transdiagnostic analyses, paving the way for more personalized and effective treatments.