UCL’s Progression Of Neurodegenerative Disease (POND) group is developing new computational models and techniques for learning characteristic patterns of disease progression using large cross-sectional data sets. Our focus is currently on dementias, such as Alzheimer’s disease, but our techniques have wider application to other diseases and developmental processes.
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.
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.
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 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.
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.
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Pete’s MRC Skills Development Fellowship aims to change the way disease modifying therapies in Huntington’s Disease are developed
Analysing over 20 years of clinical and imaging data from the Essex Memory Clinic for differential diagnosis and prognosis
Data-driven models for Progression Of Neurological Disease
Advancing our recent developments in computational modelling of Huntington’s disease (HD) to establish a staging system that can both stratify patients and estimate rate of progression and time between key pathological event
Danny’s Wellcome Trust Investigator in Science award
A sprint project to break down barriers to AI research on routinely collected healthcare data