Kilian M. Pohl

This study uses machine learning and transcriptomic integration to identify brain network and molecular signatures associated with amyloid-beta and tau in Alzheimer's disease.

This study shows early adversity increases the size of the somato-cognitive action network in the developing brain, particularly in response to threat, and this change is linked to poorer cognition.

The paper introduces a framework to quantify individualized developmental imbalance in youth using predictive normative modeling and shows it predicts psychopathological vulnerability.

This paper develops a Bayesian functional extension of the generalized synthetic control method to handle sparse and irregularly spaced biomedical data, showing improved causal inference in neuroimaging studies.

The paper uses deep learning to identify brain-behavior dimensions and biotypes in youth with ADHD and anxiety disorders, revealing distinct symptom profiles and brain development patterns.

This study examines sex-specific brain activity dynamics in youth with and without a family history of substance use disorder, finding differences in transition energy across brain networks.

This study examines how binge drinking affects gray matter volume decline in adolescents over time, finding stronger effects in younger individuals and suggesting potential for recovery with temporally proximal drinking.

The paper describes neuroimaging processing methods for the ABCD Study to extract brain phenotypes from multimodal imaging data.