Meet the team

We are a research group at the MRC Cognition and Brain Sciences Unit, School of Clinical Medicine, at the University of Cambridge

Group Leader
Alex Woolgar
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I am a Programme Leader at the MRC Cognition and Brain Sciences Unit, Professor (Grade 11) of Cognitive Neuroscience at the University of Cambridge, and an honorary Associate Professor at Macquarie University, Sydney. I am fascinated by how the firing of billions of cells in our brains gives rise to our ability to perceive, think, and act. I especially want to understand the brain mechanisms that enable humans to pay attention - underpinning our ability to behave in complex, diverse, and flexible ways. To study this I draw on a range of human brain imaging and stimulation techniques, and develop approaches that push the limits of what we can ask about how the brain works. I am honoured to get to work with the brilliant bunch of bright and enthusiastic scientists below.

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Hamid Karimi-Rouzbahani
Postdoctoral Research Fellow

With a background in Engineering, and an interest in method development through the use of machine learning in neuroimaging, I pursue three main questions in cognitive neuroscience: How does the human brain recognise visual information despite their variations? How do internal brain states such as prior knowledge, task demand and attention modulate perceptual experience? How does the brain encode sensory and cognitive processes?

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Postdoctoral Research Fellow
Jade Jackson
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I use a combination of neurostimulation (TMS) and neuroimaging (fMRI) techniques to investigate selective attention in the human brain. My previous work has focused on how and where task-relevant information comes to be prioritised in the brain (Jackson et al. Journal of Cognitive Neuroscience, 2017; Jackson et al. Cortex, 2018), and the causal influence of disrupting this prioritisation on information coding across the brain (Jackson et al., Biorxiv, 2020). My current projects involve disentangling the relationship between enhancement vs inhibition using fMRI-MVPA and using concurrent TMS-fMRI to causally link information coding to behaviour.

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Postdoctoral Research Fellow
Selene Petit

The cognitive and language abilities of minimally-verbal autistic children may currently be greatly under-estimated. My research aims at developing passive tests of language comprehension using neuroimaging, in particular electroencephalography (EEG). I used this technique during my PhD to record the brain’s electrical activity of typically-developing children while they listen to speech, and infer whether they understood the meaning of spoken sentences. I now wish to apply this promising method to minimally-verbal autistic children to learn more about their language abilities.

 
 
PhD Candidate
Dorian Minors
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I'm interested in the kinds of simple neural mechanisms that may underpin intelligent behaviour. My previous work has explored how simple neural network properties inspired by the brain might facilitate higher order aptitudes, and specifically how honey bees might solve an abstract conceptual problem thus (Cope et al. PLOS Comp. Bio., 2018). My current project explores how a popular model of decision-making may allow us to distinguish analogous computations in the human brain using MEEG.

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Christopher Whyte
PhD Candidate
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Humans have a remarkable capacity to complete complex tasks, flexibly switch between tasks, and to prioritise relevant information while excluding irrelevant information, even in novel and uncertain environments. These hallmark features of cognitive control depend upon a network of brain regions jointly termed the multiple demands system. My research uses a mixture of computational modelling and neuroimaging to reverse engineer the way in which the multiple demands system i) represents task relevant information, and ii), leverages these representations to control the flow of information throughout the rest brain.

PhD Candidate
Nadene Dermody
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My research will focus on uncovering the mechanisms through which information is exchanged between the "multiple-demand" (MD) network and more specialised regions, such as visual cortex. While the MD regions have been shown to selectively and flexibly represent task-relevant information moment- to-moment, how these regions interact with domain-specific regions to give rise to goal-directed behaviour is not yet known. My current project aims to contribute to our understanding of this by combining MEG and fMRI data, using multivariate pattern analysis techniques, to derive a spatially and temporally resolved account of how and where information is exchanged throughout the brain.

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PhD Candidate
Runhao Lu
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A distributed "multiple-demand" (MD) network across the frontoparietal brain regions is thought to be crucial for human intelligent behaviours because of its incredible function to flexibly and adaptively process task-relevant information. Although this network is commonly co-activated during demanding tasks, potential functional differentiation among MD regions have long been discussed but no clear consensus yet. My research aims to use concurrent TMS-fMRI to causally examine the distinct contributions of the individual MD regions. In particular, I ask whether these regions work differently in terms of enhancement vs inhibition during selective processing of visual information.

Lab Alumni
Collaborators