Felix Hoffstaedter

The paper identifies a distributed brain network associated with psychopathy in humans and chimpanzees, showing strong overlap with a lesion network linked to antisocial behavior.

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

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 paper introduces a deep learning tool to predict functional topography of the human visual cortex from anatomy, demonstrating its utility across diverse conditions and large-scale datasets.

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 study maps cerebellar volumetric growth from infancy to young adulthood and links it to cerebral development and socio-linguistic behaviors.

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

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.

The paper compares age-related brain deterioration between humans and chimpanzees, finding a unique positive relationship between cerebral aging and cortical expansion in humans.

The paper uses normative modeling and neurotransmitter mapping in the UK Biobank to show how age and Parkinson's disease affect brain resting state activity and connectivity through specific neurotransmitter systems.

This study evaluates how sex classification models generalize across brain connectivity datasets, finding that combining multiple datasets improves generalization performance.

The paper revises the BrainAGE method using Gaussian process regression, showing improved performance on UK Biobank data and disease-specific patterns in clinical samples.

The paper introduces BABS, a Python package for reproducible and scalable neuroimaging data processing using BIDS Apps and HPC systems.

The paper proposes methods to approximate bone mineral density and subcutaneous adiposity from T1-weighted head MRI images and validates their reliability and correlation with DXA and MRI measurements.

This study identifies a latent clinical-anatomical dimension linking metabolic syndrome severity to brain morphology and cognition in 40,087 individuals.

The paper develops non-TIV-biased models for accurate sex classification of cisgender and transgender individuals using brain morphology data.

This study systematically compares five VBM pipelines for age prediction using three large datasets, finding significant differences in their ability to quantify GMV and predict age.

This study compares the predictive power of resting-state fMRI features and individual characteristics for cognitive phenotypes in 20,000 UK Biobank subjects, finding individual characteristics generally perform better.

This paper compares 128 machine learning workflows for brain-age prediction using neuroimaging data, finding that feature representation and algorithm choice affect performance, and that brain-age delta correlates differently within-site and cross-site.

The paper evaluates how different levels of co-fluctuation in functional connectivity predict individual-level phenotypes using machine learning.

The paper shows genetic and phenotypic links between sleep, intelligence, BMI, and depression using neuroimaging and genetic data from two large datasets.

The paper introduces a framework for reproducible processing of large-scale data that complies with FAIR principles, demonstrating its performance on brain imaging and data sharing datasets.

The paper introduces a method to compute tract-specific diffusion statistics for brain white matter connections and reports age and sex-related associations in specific neural networks.

The paper investigates genetic factors influencing a neurobiological substrate for psychiatric disorders and finds that a common genetic variant is associated with the substrate but not directly with disease risk.

This paper introduces a connectivity-based framework to predict psychometric variables from brain connectivity and evaluates its performance compared to activation-based approaches.