Andreas Meyer‐Lindenberg

The paper examines neurostructural correlates of social drinking risk in alcohol use disorder using ecological momentary assessment and MRI.

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 identifies a species-conserved mechanism of hypothesis testing in rats and humans during rule learning, which enhances cognitive flexibility in complex environments.

The paper shows that reactivation strength during cued recall decreases with graph distance, only in successful retrieval, suggesting graded clustered reactivation in cognitive maps.

The paper identifies patterns of neural variability in autism using fMRI across tasks, finding greater global connectivity deviations and similar spatial patterns in autistic individuals compared to controls.

The paper identifies a multimodal brain pattern associated with autism by integrating gray and white matter morphology data and linking it to clinical symptoms.

The paper examines how social interactions affect well-being before and during the pandemic, finding that personality, neurobiology, and genes moderate these effects.

This study uses a cross-scale approach to identify how reward predictions are coded in the ventral striatum during olfactory conditioning in mice.

The paper identifies atypical brain asymmetry in autism as a potential neurophenotype for stratification, showing individualized patterns linked to symptom severity and language delay.

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

This study investigates reward processing in autism spectrum disorder (ASD) and finds generalized hypoactivity in the ventral striatum during anticipation of both social and monetary rewards.

The paper develops a phenotypic stratification model to predict autism subtypes based on SC-RRB balance and applies it to fMRI data, identifying subtype-specific neural circuitry and gene expression patterns.

This study identifies reproducible resting-state networks in the mouse brain across multiple centers using standardized rsfMRI analysis.

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.

The paper identifies reduced functional connectivity in a neural network related to pain processing in major depressive disorder using fMRI during heat pain stimulation.

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.

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

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.