Our paper showing that correlation-based connectivity measures actually largely reflect cross-subject spatial variability in the shape and location of functional brain regions is now published in eLife.
I performed my undergraduate studies at University College in Maastricht (The Netherlands). During this time I became interested in neuroscientific research, specifically using MRI and transcranial magnetic stimulation methodologies. For my final year thesis project I worked with Dr Alex Sack on a study investigating interference between manual and mental rotation, for which I was awarded a maximum grade of 100%, and which was subsequently published.
In 2006 I moved to Sheffield (UK) for a one-year MSc degree in Brain Imaging and Cognitive Neuroscience. For my MSc dissertation I analyzed rodent and human fMRI data using wavelet coherence analysis under the supervision of Dr Myles Jones. During my MSc degree and subsequent PhD research I became increasingly interested in the methodological challenges and nuances of fMRI data analysis.
Following my MSc, I moved to the Department of Psychiatry in Sheffield for my doctoral studies. Under the supervision of Dr Kwang Lee and Professor Peter Woodruff, I investigated sensorimotor timing and error correction using both fMRI and TMS. As part of my doctoral studies I was lucky to have strong collaborative links with Professor Tony Barker and Professor Simon Eickhoff.
In September 2011 I started as a Post-Doctoral Researcher at the Cognitive Affective Neuroscience group ran by Sonia Bishop at the FMRIB Centre in Oxford. Here, my work focused on identifying functional connectivity correlates of anxiety and depression. In April 2015 I moved to the Analysis group in the FMRIB Centre in Oxford, where I now work with Steve Smith on connectivity modelling in big data.
Postdoctoral Researcher in the FMRIB Analysis Group
- Course co-organiser for the FSL Course
- Course co-organiser for the FMRIB Graduate Programme
My research focuses on:
- Brain connectivity and connectomics
- Big data
The human brain is intrinsically organised into large scale connectivity networks. Effective communication within and between these networks is essential for function. Many mental disorders are thought to involve dysfunctional communication between different brain regions and networks, suggesting a large and important clinical biomarker potential of connectivity fMRI.
My work sits at the interface between analysis methods and neuroscience. In particular, I am interested in the following topics:
- Analysis methodology for connectivity, connectomics, and big data
- Use of big data in clinical neuroscience to work towards personalised medicine
- Multimodal research into the neural circuitry underlying connectivity as measured with MRI
Currently, my research primarily uses data from the Human Connectome Project and UK Biobank. I adopt and develop cutting edge analysis techniques in order to improve connectivity measures, and map the relationship between brain connectivity markers and lifestyle, health, and risk factors for disease.