Simon Bodo Eickhoff

This study links human brain functional connectivity to neurotransmitter systems using resting-state fMRI and MEG across multiple cohorts, showing consistent patterns and clinical relevance.

97% of 184 high-impact biomedical AI studies compare cross-validated models with tests assuming fold independence; in 69% of re-analyzable studies a significant comparison does not survive correction.

This study examines how hormonal changes during the menstrual cycle and under oral contraceptive use affect sleep quality and perivascular space volume, finding that LH and FSH levels are associated with sleep quality and PVSV changes.

The paper examines how different types of adversity relate to hippocampal macro- and microstructure in early adolescents, finding distinct spatial associations across brain regions.

The paper examines thalamocortical maturation by analyzing FA changes and functional connectivity in 10 thalamic nuclei across childhood and adolescence.

This study evaluates transdiagnostic latent factor models for brain-psychopathology associations using neuroimaging data, finding no consistent improvement in predictive utility over summary scores.

The paper identifies a distributed neural architecture of sustained affect that generalizes across external and internal experiences and is associated with depression symptoms.

The paper examines how age and pubertal stage independently influence the refinement of the functional cortical hierarchy during adolescence using longitudinal data.

This study compares the test-retest reliability of functional brain networks from resting-state and naturalistic viewing paradigms across two sessions, showing increased reliability with naturalistic viewing but varying by stimuli and networks.

The paper identifies multiscale principles of brain reorganization under electrical perturbation by integrating neuroimaging, connectomics, and transcriptomics data from 148 experiments.

The paper predicts brain volumes from anthropometric and demographic features using UK Biobank data, finding strong sex-based predictions for TIV and age-based predictions for other volumes.

OSCAR detects condition-related deviations in brain connectivity using a normative modeling framework, identifying task-evoked and clinical network reorganization with high accuracy.

This study links lower motor performance with poor sleep quality, depressive symptoms, and grey matter volume changes across different age groups.

This study uses machine learning on UK Biobank data to predict sleep health traits from brain imaging, finding limited predictive power despite extensive model optimization.

This study identifies two latent dimensions linking cardiometabolic health and physical robustness to brain structure and neurotransmitter systems using data from the UK Biobank.

This paper identifies nine functionally distinct subregions within the left prefrontal cortex targeted by TMS, revealing their network connectivity patterns and behavioral associations to guide individualized TMS interventions.

cuBNM accelerates brain network simulations using GPUs, enabling large-scale studies of neural processes previously limited by computational costs.

This paper introduces a method for profiling intracortical microstructure to assess laminar organisation, showing its robustness and reliability across different datasets and sites.

This paper introduces a new meta-analytic method to improve cerebellar mapping specificity and shows 54 behavioral terms converge to specific cerebellar subregions.

This paper evaluates ODF reconstruction methods for single-shell dMRI, finding SS3T provides reliable and predictive WM bundle segmentation with lower angular resolution data.

The paper examines how pre- and postnatal factors influence human cortical microstructure using MRI data from neonates, finding distinct effects on microstructural density and homogeneity.

The paper shows longer scans improve prediction accuracy in brain-wide association studies while reducing costs, with 30-minute scans being the most cost-effective.

This study identifies genetic variants influencing brain geometry using GWAS on Laplace-Beltrami spectrum eigenvalues from UK Biobank data.

This study maps cortical connectivity shifts in neonates using diffusion MRI, identifying age-dependent changes and their implications for brain development and disorders.

The paper introduces the Network Correspondence Toolbox (NCT) to evaluate spatial correspondence between novel neuroimaging results and existing brain atlases, enabling standardized reporting and comparison across studies.

The paper compares white matter organization between humans, chimpanzees, and macaques, identifying uniquely human connectivity profiles in areas related to language and abstract reasoning.

This study shows that stimulus selection affects brain activity patterns and prediction performance in individual phenotypes using naturalistic stimuli.

The paper identifies genetic and connectivity gradients that organize cortical patterning along three topographic axes.

This study compares voxel-wise and region-wise denoising in functional connectivity analyses, finding region-level denoising offers equal or better performance with reduced computational cost.

This study examines how the number of gradients used in Procrustes alignment affects identification accuracy and differential identifiability in functional connectivity analyses across multiple fMRI datasets.