Mathematical modelling of disease trajectories across modalities

Research project description

PhD will investigate mathematical modelling of disease trajectories across modalities.

A more personalised medicine is one of the major goals in society at present, and to provide better and more individualised attention, we need to characterise the heterogeneity of both the causes and the symptoms of diseases.

Focusing on the brain, we need to acknowledge (i) that brain diseases are an admixture of different conditions due to comorbidities or an aggregation of different pathologies; and (ii) that brain pathology as a dynamic process, where subjects follow heterogeneous disease trajectories.

In practice, we need statistical tools to bring these ideas from a theoretical concept to the application. To achieve a more integral understanding of brain disease, this project will develop a novel statistical framework to account for, and disentangle, the disparate sources of variability present in neural data.

We have the following goals: (i) to leverage longitudinal data to define the different aspects of a disease as a dynamic process; (ii) to integrate across complementary data sets and preprocessing choices effectively; and (iii) to provide the capacity to relate the model parameters to genetic and environmental factors.

Focusing on Alzheimer’s, we will apply our statistical framework to several public longitudinal data sets, including data from the Alzheimer Center Barcelona (ACE) in Barcelona.

Academic background / Skills

Candidates must hold a degree that allows admission to the official doctoral programme at UAB.  

Additional requirements for a stronger application are: 

• We are looking for a graduate student with interest on computational neuroscience, data analysis, coding and statistics.  
• The candidate will work on the development and application of novel machine learning approaches that use brain data to achieve a better stratification of existing diagnoses and clinical information.  
• In particular, we will design models that can bring together the different modalities existing in various large-scale datasets, leveraging concepts that lie in between the supervised and unsupervised learning paradigms. 

Research group/s description

The Research Group is currently supported by an ERC Starting Grant, a Novo Nordisk Emerging Investigator fellowship, a grant from the Independent Research Fund of Denmark and other sources of funding.

We focus on the development of statistical and machine learning techniques for the analysis and modelling of neural data across different modalities, including functional and structural neuroimaging as well as electrophysiology.

We primarily focus on human data, but we also hold collaborations with animal labs with the hope of finding a translation between animal models and the understanding of human disease.

Our work is very collaborative, working closely together with neurologists and other neuroscientists in Barcelona, Oxford, London, Aarhus and Brussels.