Stephen M. Smith

This paper introduces Mendelian randomisation methods for causal inference in neuroimaging data, using UK Biobank imaging phenotypes to demonstrate their application and limitations.

The paper develops a QSM pipeline to analyze brain magnetic susceptibility in UK Biobank data, finding associations with iron, disease, and genetic variants.

This study identifies common genetic variants influencing intrinsic brain networks using rsfMRI data from 44,190 individuals, finding associations with cognitive and clinical traits.

This paper introduces a multi-dynamic model to separately analyze time-varying brain activity and functional connectivity in resting fMRI, revealing stronger temporal changes in connectivity and better prediction of behavioral variability.

The paper uses high-resolution myelin mapping to show graded variation in cortical myelination rates along the sensorimotor-association axis during adolescence.

This study finds that adult brain age is more influenced by early life factors like birth weight and polygenic scores than by longitudinal brain decline.

This paper introduces sPROFUMO, a hierarchical model for estimating functional brain networks in individuals and populations from large fMRI datasets, showing improved performance over traditional methods.

This study identifies genetic loci associated with functional brain networks and areas using large-scale fMRI data, linking them to cognitive traits and brain disorders.

The paper describes a lightweight deep convolutional neural network for brain age prediction from MRI scans, achieving top results in the PAC 2019 challenge.

This paper introduces a multimodal independent component analysis method to discover imaging-derived phenotypes from large neuroimaging datasets, improving predictive power for phenotypic and behavioral variables.

The paper analyzes neonatal functional MRI data to study functional connectivity in term and preterm infants, finding age-related maturation and preterm birth-related disruptions in brain networks.

The paper develops normative brain volumetric development curves for term-born infants and compares preterm infants to these curves, identifying individual deviations linked to clinical factors and neurocognitive outcomes.

The paper links multimodal brain imaging markers in adolescents with physical activity and health measures using canonical correlation analysis.

The paper develops methods to assess time-varying functional connectivity in fMRI and shows it explains behavioural traits not captured by structural data or time-averaged connectivity.

The paper introduces a lightweight deep neural network, SFCN, for accurate brain age prediction using MRI data, achieving state-of-the-art performance on UK Biobank data.

This paper uses a 3D CNN to predict brain age from MRI data and finds that prediction errors correlate with clinical measurements and image-derived phenotypes.

This paper introduces an automated pipeline for processing neonatal resting-state fMRI data, improving motion correction and quality assurance in the developing Human Connectome Project.

The paper extends probabilistic functional modes to model subject variability in fMRI data, showing spatial maps capture most cross-subject variability and functional coupling reflects cognitive state.

The paper expands genetic associations with brain imaging phenotypes using updated UK Biobank data, finding more genetic variants and associations, including on the X chromosome.

The paper optimizes multi-shell diffusion MRI acquisition parameters using a data-driven approach, resulting in a specific protocol for neonatal imaging.

This paper identifies and analyzes potential confounds in UK Biobank brain imaging data, emphasizing their impact on statistical analyses and suggesting considerations for future studies.

This paper introduces a symmetric prior for regularizing nonlinear image registration, improving anatomical plausibility while maintaining or exceeding registration quality.

This study examines the relationship between mid-life and late life leisure activities and MRI measures of brain structure and functional connectivity in older adults from the UK Biobank.

The paper develops a 24-channel MRI coil and adapts HCP protocols for macaques, enabling high-quality in vivo imaging and preprocessing comparable to human studies.

The paper identifies specific amygdala connections linked to sub-clinical mental well-being markers using neuroimaging and questionnaire data.

The paper identifies 62 distinct modes of brain aging from multimodal imaging data, showing varied genetic and lifestyle associations.

The paper evaluates and optimizes network modelling methods for fMRI by testing various parcellation, connectivity measures, and classifiers across multiple datasets to find the best predictive pipeline.

The paper identifies three modes of brain co-variation in children linked to perinatal risk, socio-cognitive stratification, and urbanicity factors using brain imaging and socio-economic data.

This study identifies genetic loci specific to periventricular and deep white matter hyperintensities, revealing distinct genetic architectures and potential biological mechanisms.

The paper evaluates methods for estimating brain age delta from brain imaging data and identifies variables strongly correlated with it using UK Biobank data.