Neuroimaging, Bioinformatics & Brain Networks

Since finishing my PhD, my main focus has been the application of graph theory and other tools of network science to the analysis of brain networks derived from non-invasive neuroimaging techniques (fMRI, MEG, DTI). Our key results so far suggest that the brain is organized according to economic principles, where the cost of creating and maintaining connections is traded-off against complex topological properties (such as network efficiency, the existence of hubs and rich clubs) which may be beneficial for information processing [1].

In order to understand this trade-off more completely, I have used generative modelling to see what simple rules can give rise to specific network features [2]. I am also interested in interrogating the role of various network characteristics in cognition, using computational models as well as neuroimaging datasets which capture variation in the cognitive domain. One example is the study of datasets relating to various brain disorders. Indeed, the normal pattern of connectivity has been found to be disrupted in many disorders, including for example Alzheimer’s disease and Schizophrenia.

More recently, I have become interested in linking brain organization observed through MRI to whole-genome brain transcriptional data. For example I was able to show that adolescent maturational changes in brain structure are highly correlated with the expression of genes linked to schizophrenia [3, 4]. I believe the linking of macro- and micro-scale brain maps opens up a wide area of investigation with a wealth of unexplored challenges and opportunities.

Those interested in these topics may wish to look at:
[1] Bullmore and Sporns. The economy of brain network organization.
Nature Reviews Neuroscience, 2012,  13: 336-349

[2] Vértes, Alexander-Bloch, Gogtay, Giedd, Rapoport, and Bullmore. Simple models of human brain functional networks.
Proc. Natl. Acad. Sci. (USA), 2012, 109(15): 5868-73.

[3] Whitaker*, Vértes* et al. Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome.
Proc. Natl. Acad. Sci. (USA), 2016, 113(32): 9105-9110.

[4] Vértes et al. Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks.
Phil Trans B: Biological Sciences 2016, 371, 20150362.