Steven Williams

The paper investigates how spatial collinearity among PET receptor maps affects multivariate molecular-enriched network estimation and finds that univariate modelling is more reliable.

This study develops a deep learning model to enhance ultra-low-field MRI scans to near high-field resolution without paired high-field data, improving image quality and accessibility.

This paper uses longitudinal MRI/MRS to study demyelination in a mouse model, showing dynamic and partially reversible pathology that correlates with histology.

This study evaluates the test-retest reliability of spinal cord fMRI during sensorimotor tasks, finding poor-to-fair reliability despite consistent task performance and signal quality.

This study identifies structural brain deviations in post-COVID-19 patients using normative modelling and network analysis, linking them to TMPRSS2 expression and neural circuit disruptions.

The paper links infant gut microbiome composition and behavioral responses to maternal unpredictability, showing how microbial metabolism influences behavioral outcomes.

This paper identifies altered dynamic brain states in Post-COVID-19 Syndrome using resting-state fMRI, showing reduced attentional states and increased limbic-DMN states linked to cognitive impairment and inflammation.

The paper integrates transcriptome-wide association studies with brain atlases to map polygenic risk onto structural brain changes in psychiatric disorders, finding significant correlations in some disorders but not others.

This study uses multimodal MRI to find altered brain perfusion and oxygen metabolism in PCS, linked to cognitive and emotional symptoms.

MegaTrack provides a framework for efficient and anatomically accurate analysis of large-scale tractography datasets, enabling rapid dissection and metric extraction with high reproducibility and reduced processing time.

This study evaluates the reliability and correspondence of ultra-low-field MRI morphometry to high-field MRI, showing high reliability and correspondence across scan resolutions and contrasts.

This paper develops a deep learning model to enhance ultra-low-field MRI scans for pediatric neurodevelopmental research, achieving better reconstruction of brain structures than existing methods.

This study examines how anhedonia severity affects attentional network recruitment and emotional blunting during music listening in healthy adults.

The paper evaluates factors affecting brain-age prediction models using structural neuroimaging data from 35,683 individuals, finding optimal model performance with no site harmonization, age-binning, and a sample size of over 1,600.

The paper predicts anorexia nervosa and autistic characteristics from resting state cyclic connectivity in individuals with anorexia nervosa using rsfMRI data.

This paper extends the REACT method to ASL fMRI, showing it can detect molecular-enriched functional connectivity comparable to BOLD fMRI, with complementary information.

This paper introduces a novel spinal fMRI method to study functional connectivity in the cervical spinal cord, finding mixed test-retest reliability but consistent spatial organization across sessions.

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.

This study uses REACT to examine how propofol affects neurotransmission-related functional connectivity during rest and naturalistic stimulation, revealing changes in GABAergic, noradrenergic, and cholinergic networks.

This paper introduces hypervoxels to improve neuroimaging analysis by integrating spatial and anatomical information from diffusion MRI data, enhancing detection of white matter effects in ALS.

The paper examines age-related variations in cortical thickness across different life stages using gene expression data from a large neuroimaging dataset.

This paper identifies neurotransmission-related functional connectivity biomarkers in chronic pain patients and shows their predictive value for analgesic response to duloxetine.

The paper uses imaging transcriptomics to link neuroimaging markers with transcriptomic and cellular correlates in the human brain, showing robust results across different parcellation methods.

This study identifies cortical morphometric similarity remodelling in chronic pain and links it to transcriptomic changes in glial and neuronal genes across multiple pain syndromes.

This paper demonstrates that multi-parametric manganese-enhanced MRI (MEMRI) can detect senile plaques in rodent AD models, showing manganese binds to plaques in 5xFAD mice but not TgF344-AD rats.

This study uses longitudinal fMRI to examine cognitive control development in children with ASD or OCD and typically developing children, finding similar cognitive control development despite some neural differences in OCD.

The paper analyzes cortical thickness trajectories in 17,075 individuals to show peak thickness in childhood, steep decline in early adulthood, and slower decline later, with notable exceptions and significant inter-individual variability.

The paper models fMRI noise volatility using financial mathematics methods, finding it to be highly irregular with Hurst parameter H < 0.5.

This paper evaluates rapid processing of multicontrast EPImix scans and shows that quantitative neuroimaging data can be obtained within minutes, enabling adaptive imaging protocols.