Ashlea Segal

The paper develops a data-driven method to identify individualized disordered connectivity patterns from fMRI data, revealing diverse neurobiological mechanisms underlying transdiagnostic psychiatric symptoms.

The paper examines brain network dynamics in individuals with and without psychiatric diagnoses, finding that these dynamics can differentiate symptom profiles more effectively than diagnostic categories.

The paper presents the MIND model, showing that competing induction and exclusion programs shape the sensorimotor-to-association axis in the neocortex across species.

The paper examines white matter volume deviations in psychiatric disorders using normative modeling, finding heterogeneous patterns with some aggregation in autism and schizophrenia.

This paper identifies widespread functional connectivity differences in cocaine use disorder (CUD) and links these patterns to dopamine D2/3 receptor densities across the brain.

This paper introduces mode-based morphometry (MBM), a multiscale method for analyzing neuroanatomical variability, showing it is as valid or more reliable than traditional techniques for cortical thickness maps.