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Accounting for heterogeneity in network neuroscience: extending the Bayesian Exponential Random Graph Model to infer and identify group differences

  • Full or part time
    Dr S White
    Dr B Tom
    Dr B Lehmann
  • Application Deadline
    Tuesday, January 07, 2020
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

Background:
Network Neuroscience is the study of the structure and function of the brain through the lens of networks and network constructs [1,2]. This approach has yielded many insights over the last decade, for example identifying brain-based network biomarkers such as the small-worldness property and its association with various clinical outcomes or ability to classify individuals with autism or schizophrenia [3].

There is much active research in developing new ways to record and analyse brain signals and structure across multiple modalities, as well as developing new theoretical frameworks and computational tools to make inference from this wealth of data [4].

One potential framework is to use Exponential Random Graph Models (ERGMs) to capture the complex structure and make inference. These ERGMs act as a low dimensional summary of the large and complex brain networks.

The aim of this project is to extend an existing Bayesian ERGMs framework [5] to account for the complexities and structures often seen in neuroimaging studies in order to capture heterogeneity.

Project:
The project will build on an existing Bayesian Exponential Random Graph Model applied to healthy ageing using the The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study.

The project will work to extend the model to account for additional structural heterogeneity, namely aspects of study design that should be accounted for, and individual level heterogeneity, by accounting for additional confounders within the ERGM framework.

There are open questions on computational scaling of ERGM approaches, and there will be scope to investigate these issues by applying the method to data from the UK Biobank which contains thousands of individuals with multiple imaging modalities.

The project will be hosted in the MRC Biostatistics Unit in Cambridge, with visits and a potential short research visit to Oxford.

Funding Notes

The MRC Biostatistics Unit offers 4 fulltime PhDs funded by the Medical Research Council for commencement in April 2020 (UK applicants only) or October 2020 (all applicants). Academic and Residence eligibility criteria apply.

In order to be formally considered all applicants must complete a University of Cambridge application form. Informal enquiries are welcome to

Applications received via the University application system will all be considered as a gathered field after the closing date 7th January 2020

For all queries see our website for details View Website

References

1. Bullmore, Sporns (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. doi: https://www.nature.com/articles/nrn2575
2. Bassett, Sporns (2017). Network neuroscience. Nat Neurosci. https://www.nature.com/articles/nn.4502
3. Morgan, White, Bullmore, Vértes (2018). A Network Neuroscience Approach to Typical and Atypical Brain Development. Biol Psychiatry Cogn Neurosci Neuroimaging. https://doi.org/10.1016/j.bpsc.2018.03.003
4. Bassett, Zurn, Gold (2018). On the nature and use of models in network neuroscience. Nature Reviews Neuroscience. https://www.nature.com/articles/s41583-018-0038-8
5. Lehmann, Henson, Geerligs, Cam-CAN, White (2019). Characterising group-level brain connectivity: a framework using Bayesian exponential random graph models. bioRxiv. https://doi.org/10.1101/665398

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