UCL’s Progression Of Neurodegenerative Disease (POND) group develops new computational models and techniques for learning characteristic patterns of disease progression using large cross-sectional data sets. Our primary focus is on dementias caused by diseases such as Alzheimer’s, but our techniques have wider application to other diseases and developmental processes.

Some background reading on “POND modelling”:

We are considering a broad approach to modelling disease progression, starting with Hubert Fonteijn’s work in NeuroImage 2012 on event-based models, and also exploring the possibility of determining causal links between events. Events constitute biomarker abnormality, which includes image-based biomarkers such as regional atrophy in the brain, as well as biomarkers such as levels of abnormal proteins in cerebrospinal fluid.

All the while, we ensure clinical relevance in the models through collaboration with the Dementia Research Centre at UCL’s Institute of Neurology.

UCL POND started with an EPSRC-funded project (see our projects for more details) and are coordinators of the EuroPOND (a Horizon 2020 project) and E-DADS (EU JPND) consortia.

Research Vision

The Challenge

The global ageing population has placed neurodegenerative diseases among the biggest public health challenges of 21st century healthcare. It is vital to understand this spectrum of diseases on both mechanistic and phenotypic levels to elucidate differences and similarities that can inform diagnosis, prognosis, monitoring, therapy development, and treatment & care decisions.

The Vision

Our vision in the POND initiative at UCL is to provide new avenues for understanding the complexity of clinical phenotypes of multifactorial neurological diseases. Disentangling this complexity by identifying signatures of each disease is essential for meeting the challenge.

The Means

The platform upon which we will build the tools for achieving this vision is data-driven computational-and-statistical modelling, a set of powerful approaches with the ability to provide fine-grained and uniquely holistic pictures of neurological disease progression. Such emerging technologies will underpin support systems for clinical and drug-development applications, specifically by enabling precision medicine through differential diagnosis, patient staging, and personalised prognosis.

The Strategy

Our strategy for achieving impact within our vision requires a balance between model utility and complexity. Model utility is the end-game focus in order to impact disease management across the full spectrum from patients to medical health professionals and drug-development companies. Model complexity is unavoidable due to the nature of the disease signatures we seek, and requires methodological development, which is one of our group’s strengths.

Team

Principal Investigators

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Prof Danny Alexander

PI, Co-Founder

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Dr Neil Oxtoby

PI, Co-Founder, UKRI Future Leaders Fellow

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Dr Alexandra Young

PI, Co-Founder, Wellcome Trust Fellow

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Dr Arman Eshaghi M.D.

NIHR Advanced Fellow

Research Associates/Fellows

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Dr Ellie Thompson

Research Fellow in Brain Connectivity Mapping

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Anna Schroder

Research Fellow in AI tools for Hippocampal Segmentation

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Dr Hanyi Chen

Senior Research Fellow

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Dr Maitrei Kohli

Research Fellow in Machine Learning Tools for Huntington’s Disease

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Bojidar Rangelov

Research Fellow

Dr Moona Mazher

Research Fellow in Machine Learning and Modelling in Neurodegenerative Disease.

Dr Sonja Soskic

Research Fellow in data management, analysis, and modelling for the CU-MONDAI project.

PhD Students

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An Zhao

PhD Student

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Seymour Lopez

PhD Student

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Isaac Llorente Saguer

PhD Student

Understanding & learning stuff, poka yoke

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Ms Zeena Shawa

MRes Student

Powerlifting, Art, Video Games

Ahmed Abdulaal Abdulaal

PhD Student (i4health 2021-22 cohort)

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Beatrice Taylor

PhD Student

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Liza Levitis

PhD Student

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Tiantian He

PhD Student

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Mihaela Croitor

PhD Student in Computational modelling of disease progression & subtype discovery in Alzheimer’s disease

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Gonzalo Castro Leal

PhD student.
Previously — Research Assistant in Memory Clinic Image Computing CODEC

Co-Investigators

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Prof Frederik Barkhof

Chair of Neuroradiology, UCL IoN & Co-I E-DADS

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Prof Zuzana Walker

Professor of Psychiatry of the Elderly, UCL Division of Psychiatry

Visitors

Eline Vinke

Visited Nov 2018 – Jan 2019

Riccardo Pascuzzo

Visited Nov 2016 – Jan 2017

Dr Esther Bron

Visited Sep 2015 – Dec 2015

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Yaofeng Chong

Visited May 2022 – May 2023

Alumni

Dr Razvan-Valentin Marinescu

PhD student => Postdoc at CSAIL, MIT (Oct 2018)

Dr Nicholas Firth

Research Associate => Industry (Oct 2018)

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Dr Peter Wijeratne (he/him)

MRC Skills Development Fellow => University of Sussex PAL (Mar 2023)

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Maura Bellio

PhD Student (defended May 2021) => Industry

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Dr Leon Aksman

Research Associate => USC faculty (Nov 2020)

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Dr Sara Garbarino

Research Associate => Research Fellow at Inria Sophia Antipolis (2018)

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Kyriaki (Rica) Mengoudi

PhD student (defended March 2021) => Industry

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Pere Planell Morell

PhD Student (defended September 2022)

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Dr Daniele Ravi

Senior Research Associate => University of Hertfordshire faculty (Jan 2020)

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Nonie Alexander

PhD Student (Defended 2022)

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Dr Le Zhang

Research Fellow => Oxford postdoc (Jun 2022)

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Mar Estarellas Garcia

PhD Student (defended June 2023)

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Will Scotton

PhD Student (defended May 2023)

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Dr Cameron Shand

Research Fellow in Disease Progression Modelling and Machine Learning for Clinical Trials => Francis Crick Institute

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Dr Eda Bilici Ozyigit

Research Fellow in Medical Informatics and Machine Learning

Collaborators

Dr David Cash

Principal Research Fellow, UCL IoN

Dr Rimona Weil

Principal Research Fellow, Weill Lab, UCL IoN

Dr Joseph Jacob

Principal Research Fellow

Dr Jacob Vogel

Postdoctoral Fellow, PennLINC

Prof Olga Ciccarelli

Professor of Neurology, IoN

Dr Jon Huang

Research Scientist, Google

Recent Publications

Filter and view more publications here.

The temporal event-based model: Learning event timelines in progressive diseases

Timelines of events, such as symptom appearance or a change in biomarker value, provide powerful signatures that characterise …

Four distinct trajectories of tau deposition identified in Alzheimer’s disease

Alzheimer’s disease (AD) is characterized by the spread of tau pathology throughout the cerebral cortex. This spreading pattern was …

Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data

Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these …

Sequence of clinical and neurodegeneration events in Parkinson's disease progression

Projects

EuroPOND

Data-driven models for Progression Of Neurological Disease

E-DADS: Early Detection of Alzheimer’s Disease Subtypes

Data-driven models for Progression Of Neurological Disease

I-AIM: Individualised Artificial Intelligence for Medicine

Neil’s UKRI Future Leaders Fellowship

PASSIAN: Piloting A Secure, Scaleable Infrastructure for AI in the NHS

A sprint project to break down barriers to AI research on routinely collected healthcare data

MiBirth: Magnetic Resonance Imaging in late pregnancy

Using MRI in late pregnancy to help predict the best mode of birth for an individual mother and baby.

Past Projects

A list of our past projects

POND Conferences

Since 2014 the UCL POND team has convened a biennial workshop/conference meeting focussed on data-driven modelling of disease progression. The first few were part of the EuroPOND consortium that we led.

For more information, see pondmodels.net.