Emanuel Schwarz

Predicting alcohol consumption from resting-state functional connectivity: Feature relevance and the potential of meta-matching.

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

Thalamic Nuclei Volumes Across Psychiatric and Neurological Disorders: A Multi-Site Magnetic Resonance Imaging Study

This study compares thalamic nucleus volumes across multiple psychiatric and neurological disorders using MRI, finding significant volume reductions in specific regions associated with different conditions.

Generating Synthetic Task-based Brain Fingerprints for Population Neuroscience Using Deep Learning

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.

Chronic pain is linked to a resting-state neural archetype that optimizes learning from punishments

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

dsMTL - a computational framework for privacy-preserving, distributed multi-task machine learning

The paper introduces dsMTL, a framework for privacy-preserving, distributed multi-task machine learning, and evaluates its performance in identifying biological signatures across distributed datasets.

The genetic architecture of human brainstem structures and their involvement in common brain disorders

The paper identifies genetic loci associated with brainstem volumes and finds genetic overlaps with psychiatric and neurological disorders.

Genetic control of variability in subcortical and intracranial volumes

The paper identifies genetic loci associated with variability in brain volumes and thickness, showing they are distinct from loci affecting mean volumes.

Genetics of brain age suggest an overlap with common brain disorders

The paper estimates brain age gaps using MRI and machine learning in individuals with and without brain disorders, finding heritability and genetic overlap with common disorders.

Brain state stability during working memory is explained by network control theory, modulated by dopamine D1/D2 receptor function, and diminished in schizophrenia

The paper shows working memory involves brain-wide state switching, with dopamine D1/D2 receptor expression affecting stability and state transitions, and schizophrenia linked to altered network control properties.

Generative network models identify biological mechanisms of altered structural brain connectivity in schizophrenia

The paper uses generative network models to identify spatial and topological mechanisms underlying altered structural brain connectivity in schizophrenia and its relatives.

The dark side of the mean: brain structural heterogeneity in schizophrenia and its polygenic risk.

This paper investigates brain structural heterogeneity in schizophrenia and finds that polygenic risk is associated with thinner fronto-temporal cortices and smaller hippocampal subfields, but not with increased variability.