Andreas Heinz

This study uses machine learning to predict alcohol consumption from resting-state fMRI connectivity, finding weak but reproducible signals with limited predictive power.

This study investigates Pavlovian-to-instrumental transfer (PIT) interference in individuals with alcohol use disorder (AUD) and smoking status, finding greater PIT interference and reduced prefrontal activation in AUD participants.

The paper proposes the amygdala burnout hypothesis, showing early-life stress leads to initial amygdala volume increases in adolescence followed by reductions in older age across rodents and humans.

The paper examines alcohol-specific and general Pavlovian-to-instrumental transfer (PIT) in individuals with alcohol use disorder (AUD) and controls, finding stronger alcohol-specific PIT in AUD.

The paper identifies two reproducible reward imaging phenotypes linked to dopamine D2 receptor function and shows differential responses to antipsychotic drugs.

The paper examines how childhood adversity dimensions relate to frontostriatal network connectivity and anhedonia in young adults, finding sex-specific associations.

The paper uses longitudinal fMRI data to show that proactive and reactive control networks stabilize after mid-adolescence, predicting cognitive stability and psychopathology.

This study examines how different types of adversity affect grey and white matter development in adolescents across two cohorts, finding distinct associations between adversity types and brain structure metrics.

This study shows estrogen receptor alpha in the amygdala influences alcohol craving in females through mouse models and human genetics.

The paper uses longitudinal neuroimaging and behavioural data to show how brain-behaviour relationships shift from externalising to internalising symptoms across adolescence, identifying age 14 as a critical transition point.

This study links bullying victimization to structural brain changes in adolescents and young adults, showing sex-specific effects on brain regions involved in emotion and motor function.

The paper develops a digital twin brain model that simulates and manipulates functional brain networks to predict mental illness symptoms and responses to interventions.

The paper introduces dGAMLSS, a distributed algorithm for fitting GAMLSS models to construct population reference charts without sharing patient data.

MOCHA identifies latent population subgroups with distinct genetic effects from multi-omics data, revealing two neurodevelopmental clusters linked to adolescent inhibitory control.

The paper introduces DeepTaskGen, a deep learning method that generates synthetic task-based contrast maps from resting-state fMRI data, outperforming benchmarks and enabling biomarker development for cognitive tasks.

This study maps the coupling between cortical geometry and white matter tract connectivity, showing how these aspects jointly shape brain function.

This study identifies two ASD subgroups with distinct brain morphology using cross-cultural datasets, highlighting key brain regions associated with subgroup characteristics and autistic traits.

The paper identifies a neural archetype associated with chronic pain susceptibility by analyzing resting-state networks in population and clinical samples.

The paper introduces Brain-DiT, a deep generative model for analyzing task-fMRI data, identifying cognitive regions and subgroups linked to psychiatric conditions like depression and alcohol abuse.

The paper conducts GWAS on brain volumes, identifies genetic loci, and develops polygenic scores with cross-ancestry predictive ability and causal links to neuropsychiatric disorders.

The paper identifies a genetic-dependent brain signature of resilience linked to childhood abuse and depression risk in girls, showing its predictive value for emotional disorders in adolescence and early adulthood.

The paper links regional human cortex development and aging to molecular and cellular brain organization patterns using neuroimaging and gene expression data.

This study examines how variability in brain state engagement changes with age and its relationship to executive function in youth.

This study shows that a brain network for sustained attention at age 14 predicts increased cannabis and cigarette use by age 23 in a longitudinal cohort.

The paper examines sex differences in subcortical brain volume trajectories across adolescence using longitudinal data from two European sites.

The paper examines functional brain network organization in alcohol-dependent patients to identify predictors of relapse during a 12-month follow-up.

The paper introduces a computationally efficient method to measure brain network flexibility using fixed modular frameworks, showing it matches results from a data-driven method with less computational cost.

This study uses federated analysis in COINSTAC to examine functional network connectivity in adolescent brains linked to smoking and alcohol consumption, finding hypoconnectivity and reduced synchronization.

The paper investigates how copy number variants in circadian pathway and insomnia risk genes contribute to ASD risk and sleep disturbances in children with ASD.

The paper introduces brain charts as a standardized resource for benchmarking brain morphology across the human lifespan using MRI data from a large and diverse cohort.