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White Matter Lesion project

Project team


  • Sarah Al-Bachari1 (PI)


  • Hamied Haroon2 (PI)


  • Anette Schrag1 (Parkinson’s Disease Specialist)


  • Shauna Angell1 (Research Assistant)

1UCL Queen Square Institute of Neurology, Department of Clinical and Movement Neurosciences, University College London, London, UK

2School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK; Geoffrey Jefferson Brain Research Centre, The University of Manchester, Manchester, UK

Project summary

The ENIGMA-PD White Matter Lesion (WML) project aims to understand the vascular contributions to Parkinson’s disease (PD) through analysis of WML patterns and their associations with clinical features. This project began by comparing automated WML segmentation approaches in PD to ensure accurate WML maps. We then developed the containerised ENIGMA-PD-WML pipeline which combines pre-processing and post-processing steps with the UNet-pgs algorithm to segment WML from an individual’s T1-weighted and FLAIR images, outputting binary WML maps in both native and MNI standard space. The aim is to provide a streamlined, standardised approach to WML segmentation and allow sharing of non-identifiable data between sites, initially within the ENIGMA-PD consortium. The project was launched in Spring 2025; several sites have successfully run the pipeline and we have performed initial analyses, with an aim to complete analysis of the full dataset by Autumn 2026.

The ENIGMA-PD-WML project is the first stage of the wider ENIGMA-PD-Vasc project, which aims to incorporate a range of vascular neuroimaging markers to unravel the complex vascular contributions to PD pathophysiology.

Read more about this project in the full secondary proposal.

Link to repositories: UCL-ARC/Enigma-PD-WML: Segment White Mater Lesions (WML) in T1-weighted and FLAIR MRI images using FSL and U-Net

Preprint on Comparison of Automated WML Segmentation Approaches in PD: Comparison of Automated White Matter Lesion Segmentation Approaches for Use in Large, Multi-Site Data Analyses in Parkinson’s Disease | bioRxiv

Preliminary analyses of already collated data presented at the 2026 AD/PD™ Alzheimer’s Disease and Parkinson’s Disease Conference

Look out for our upcoming publication on the ENIGMA-PD-WML Pipeline Toolkit Paper!

Included Cohorts:

  • Institute of Biomedicine of Seville, Spain
  • Karolinska Institutet, Sweden
  • Medizinischen Universität Graz, Austria
  • National Institute of Health, Armenia
  • National Neuroscience Institute, Singapore
  • National Taiwan University, Taiwan
  • Parkinson’s Progression Markers Initiative
  • Sao Paolo University Hospital Milan, Italy
  • Stanford University, USA
  • University at Buffalo, USA
  • University of Canterbury, New Zealand
  • University of California, San Francisco, USA
  • University of Pennsylvania, USA

Contact Details:

Sarah Al-Bachari: s.al-bachari@ucl.ac.uk

Hamied Haroon: hamied.haroon@manchester.ac.uk

Shauna Angell: shauna.angell.24@ucl.ac.uk