André Aleman

This study investigates whether transcranial temporal interference stimulation (tTIS) modulates occipital alpha power compared to sham and carrier control, finding significant increases with tTIS.

This study identifies small but consistent anatomical correlates of negative symptom dimensions in schizophrenia, linking them to prefrontal cortical thickness and limbic subcortical volumes.

The paper investigates how fear affects adaptation to environmental volatility using computational modeling and neuroimaging, finding that fear suppresses adaptive learning in volatile environments.

The paper investigates the temporal dynamics of emotion-cognition integration using EEG, finding distinct theta and alpha oscillation patterns for emotion processing, cognitive processing, and their integration.

The paper investigates how molecular and connectomic vulnerabilities contribute to cortical abnormalities across multiple brain disorders using MRI data from 47,000 individuals.

This study challenges the traditional view of the default mode network (DMN) as solely task-negative by showing its involvement in low-effort cognitive processing and intermittent interactions with the extrinsic mode network (EMN).

The paper investigates how environmental volatility affects learning and brain organization, finding asymmetric behavioral and neural adaptations when transitioning from volatile to stable environments.

This paper uses connectome-based predictive modeling to predict individual anxiety levels from resting-state functional connectivity data in healthy participants, identifying key brain regions and their connectivity patterns.

The paper predicts individual schizophrenia symptom dimensions using functional brain networks and links these networks to molecular features like dopamine and serotonin systems.

This paper examines brain aging in major depressive disorder (MDD) using a large dataset, finding that MDD patients show increased brain age differences compared to controls.

This study identifies subcortical shape differences in MDD patients using ENIGMA data, highlighting region-specific changes in surface area and thickness.

The paper uses machine learning to rank cognitive domains based on brain data to predict schizophrenia in a multi-site dataset.

This work uses machine learning to automate quality control of 3D brain shape models, reducing human workload by 30-70% across multiple datasets and diseases.