Fidel Alfaro‐Almagro

DHARM is an open-source tool that diagnoses, harmonizes, and evaluates batch effects in MRI data using standardized workflows and multivariate diagnostics.

This paper addresses bias in cortical thickness estimation in UK Biobank by using harmonisation methods to correct for batch effects caused by missing T2-FLAIR data.

This paper constructs internally-consistent, fully-unbiased multimodal MRI brain templates from UK Biobank data, showing improved registration accuracy compared to existing templates.

The paper improves prediction of cognitive traits from fMRI data by stacking multiple dynamic brain network models to reduce variability and enhance accuracy.

The paper develops a resource to evaluate MRI harmonisation approaches by comparing variability across scanners, within-scanners, and biological differences using multimodal data from multiple sites.

The paper uses neuroimaging data from the UK Biobank to analyze functional connectivity between interoceptive brain regions and links it to health-related domains using canonical correlation analysis.

This paper generalises the HCP multimodal cortical parcellation to UK Biobank data using a geometric deep learning framework, improving detection rates across cortical areas.

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

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.

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.

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

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.

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 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 develops a Bayesian spline method to model the spatio-temporal distribution of white matter hyperintensities with respect to age and validates it on simulated and real datasets.

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.

The paper evaluates phrenology using modern neuroimaging techniques and lifestyle data from a large cohort, finding no support for linking scalp morphology to personality traits.

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

This paper introduces BIDS Apps, a framework for creating portable neuroimaging analysis tools compatible with BIDS data, improving reproducibility and ease of use.