Ludovica Griffanti

Diagnose Harmonisation (DHARM): An open-source tool for the harmonisation and analysis of batch effects in MRI-derived measures.

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

Comparison of Automated White Matter Lesion Segmentation Approaches for Use in Large, Multi-Site Data Analyses in Parkinson's Disease

This paper evaluates automated white matter lesion segmentation methods in Parkinson's disease and healthy controls, finding U-Net-pgs to be the most accurate and reliable approach across multiple metrics and datasets.

The ENIGMA-PD-WML Pipeline: A Containerized, User-Friendly Approach for Accurate, Standardized Segmentation of White Matter Lesions in Multi-Site MRI Data

Tackling Bias in Cortical Thickness Estimation in UK Biobank Using Harmonisation Approaches

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.

Normative models for neuroimaging markers: Impact of model selection, sample size and evaluation criteria

This study evaluates normative models for neuroimaging data, showing that larger sample sizes reduce variance but not bias, and flexible models perform better for nonlinear data.

Identifying microstructural changes in diffusion MRI; How to circumvent parameter degeneracy

This paper introduces BENCH, a Bayesian framework to infer parameter changes in biophysical models despite parameter degeneracies, applied to diffusion MRI data.

Intrinsic Network Activity Reflects the Ongoing Experience of Chronic Pain

The paper analyzes how intrinsic brain network activity fluctuates with pain intensity in chronic pain patients using fMRI and identifies specific network changes associated with different pain states.

Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images

This study compares domain adaptation techniques to improve white matter hyperintensity segmentation across different MRI datasets, finding that unsupervised domain adversarial training performs best.

Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images

This paper proposes a triplanar ensemble U-Net model for WMH segmentation on MR images, achieving robust performance comparable to existing methods.

Integrating large-scale neuroimaging research datasets: harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets

This paper harmonises white matter hyperintensity measurements across two neuroimaging datasets by standardising preprocessing and modelling demographic and physiological differences.

Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study

The paper predicts brain age using multimodal MRI data from the Whitehall II cohort and links brain aging to cardiovascular risk factors.

Automated lesion segmentation with BIANCA: impact of population-level features, classification algorithm and locally adaptive thresholding

This paper improves the BIANCA tool for WMH segmentation by introducing a locally adaptive thresholding method, LOCATE, which significantly enhances segmentation performance compared to global thresholding.

Common genetic variation indicates separate etiologies for periventricular and deep white matter hyperintensities

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

Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference

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.

Image Processing and Quality Control for the first 10,000 Brain Imaging Datasets from UK Biobank

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