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 Track Scientist at the MRC Cognition and Brain Sciences Unit 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.

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?

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.

Postdoctoral Research Fellow
Alyse Brown
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I am focused on creating more flexible methods to measure language comprehension in minimally-verbal children on the autism spectrum by using passive measures such as electroencephalography (EEG). This research has also led to an interest into why well-established ERP effects are unreliable at the single subject level. My PhD was completed in 2018 in the field of cognitive neuroscience at La Trobe University Australia where I researched the temporal non-linearities in primary visual cortex (Brown et al. Frontiers in Human Neuroscience 20182019) and visual processing in autism. My general research interests include visual perception, developmental disorders, ageing and semantic processing. If you would like to read my work, you can find it at: Google Scholar

 

 
 
 
Catriona Scrivener
Postdoctoral Research Fellow
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My current research focuses on the top-down role of the multiple demand network on visual cortex activation using combined TMS-fMRI. I am also interested in method development for multimodal neuroimaging, and my previous experience includes combined EEG-fMRI and GVS-EEG (galvanic vestibular stimulation). I completed my PhD earlier this year, which focused on the neural signatures of visual awareness and change blindness.

Lydia Barnes
PhD Candidate
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For me, one of the most interesting things about human cognition is how many little mistakes and attention-lapses we make in our daily activities. I remember reading about everyday action slips and thinking, why do we lose track of our goal when it's so simple? We now know that the ‘multiple demand’ brain network is critically involved in goal-directed behaviour. I use M/EEG to measure what this network codes at each moment in a task, to understand how the brain supports dynamic focus on the current goal.

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.

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.

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.

Christopher Whyte
PhD Candidate

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.

Lab Alumni
Collaborators
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MRC Cognition and Brain Sciences Unit
School of Clinical Medicine
University of Cambridge

© 2020 Woolgar Lab