Stephen M. Smith

The paper addresses the issue of biased standard errors in Pearson's correlation coefficient for fMRI time series with autocorrelation, proposing a new xDF method to improve rsFC inferences.

This paper evaluates FNIRT, a nonlinear image registration tool, showing its effectiveness in handling intensity differences and producing diffeomorphic warps across simulated and experimental data.

The paper revisits the relationship between spatial configuration and functional connectivity, showing spatial overlap biases connectivity estimates and introducing an improved method to address this bias.

The paper uses MRI data to show that white matter microstructure predicts functional connectivity between brain regions, with genetic variants linked to this relationship.

The paper derives age-related normative percentiles for hippocampal and grey matter volume using UK Biobank data to aid in clinical and research assessments of brain health.

This paper presents GPU-accelerated frameworks for diffusion MRI applications, achieving significant speedups in microstructure estimation and tractography.

This study identifies genome-wide significant genetic loci for handedness and links them to microtubule-related genes associated with brain lateralization and neurodevelopmental disorders.

This paper introduces temporal ICA to remove global structured noise from fMRI data while preserving global neural signals, improving upon traditional global signal regression methods.

This study performs genome-wide association analyses on brain imaging phenotypes from UK Biobank, identifying 148 significant SNP-imaging associations with potential relevance to neurological and psychiatric disorders.

This paper introduces a denoising method for multi-site multimodal MRI data to remove scanner and site variability, improving statistical analysis and data integration.

This paper introduces dynamic graphical models for directed functional connectivity and evaluates their performance using simulated and human fMRI data.

This paper introduces the Additive Signal Change (ASC) method to improve the specificity of functional connectivity analysis by disambiguating signal changes underlying neural activity correlations.

This study examines how the spatial arrangement of brain regions influences functional connectivity and behavioral predictions.

The paper introduces a fully automated pipeline for neonatal cortical surface reconstruction from MRI, achieving high accuracy in 85% of cases.

This paper introduces a new regularization method for cortical surface alignment in brain imaging, improving the robustness and biological interpretability of multi-modal data alignment.

The paper describes an automated processing and quality control pipeline for brain imaging data from the first 10,000 UK Biobank participants.