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Rebecca Jackson
Research fellow (British Academy)

Rebecca.Jackson@mrc-cbu.cam.ac.uk
01223 769452

Research Interests

My research utilises a two-fold multi-method approach to explore how brain architectures give rise to cognitive functions. I develop and employ connectivity-based imaging approaches that emphasise interpretability and allow formal links between brain networks and function. Simultaneously, I promote a ‘reverse engineering’ approach to computational modelling whereby the functional consequences of specific architectural features are formally determined. Together these approaches help bridge the explanatory gap between function and the brain, and encourage cross-fertilisation as hypotheses generated through each method are fed back to the other.

I principally apply this approach to the domain of semantic cognition, or conceptual knowledge, investigating the architecture of the semantic cognition network and how this allows for positive interactions between antagonistic control and representation processes. I aim to uncover the fundamental principles dictating the relationship between architectural properties of the cortex and their cognitive and behavioural consequences. For a full list of publications see my Google Scholar page.

 

I currently have two PhD students:

Vicki Hodgson (BBSRC DTP studentship) - Investigating the role(s) of posterior lateral temporal cortex in semantic cognition and across domains

Setareh Rahimi (Cambridge Trust International Scholarship) - Uncovering Dynamic Interactions between Semantic Representation and Control Networks Using Novel Multivariate Approaches for EEG/MEG Connectivity

Previous students include:

Emer Jones (BBSRC DTP rotation student) - Establishing the neural architecture of executively-constrained semantic control: convergent evidence from in vivo human MRI structural white-matter connectivity

Matthew Rouse (Masters project student) - Functional connectivity-based parcellation of the primary somatosensory cortex and the effect of synchronous co-activation

 

I am seeking PhD students interested in using a range of methodological approaches (including task and resting-state fMRI, TMS, tractography, MEG, computational modelling) to answer questions broadly connected to the domains of semantic cognition, language and memory. I am particularly interested in projects related to the organisation and function of the medial prefrontal cortex, top-down semantic cognition, improving TMS methodologies and the computational principles underlying cognition. Interested applicants should email me directly to discuss project ideas.

 

Biography

I obtained my PhD on ‘Temporal and Spatial Dynamics of the Semantic Network: Explorations using Transcranial Magnetic Stimulation (TMS) and fMRI’ from the University of Manchester, for which I was awarded the 5th annual Frith Prize by the Experimental Psychology Society. Since this time I have secured two competitive fellowships to continue my research programme. In my EPRSC doctoral prize, I studied the semantic network using functional and structural connectivity methods.

I am currently completing a British Academy postdoctoral fellowship at the MRC Cognition and Brain Sciences Unit focussed on ‘Understanding How the Semantic System can Both Generalise and be Selective: Developing a Full Computational Account of Semantic Cognition and its Disorders’.

CBSU publications
JACKSON, R. (In Press) The Neural Correlates of Semantic Control Revisited, NeuroImage [Read More]

HUMPHREYS, G.F., JACKSON, R. L., LAMBON RALPH, M.A. (2020) Overarching principles and dimensions of the functional organisation in the inferior parietal cortex, Cerebral Cortex, 30(11):5639-5653 [Open Access]

JACKSON, R. , Bajada, C., LAMBON-RALPH, M. & Cloutman, L (2020) The graded change in connectivity across the ventromedial prefrontal cortex reveals distinct subregions, Cerebral Cortex, 30(1):165-180 [Open Access]

JACKSON, R. L., Cloutman, L.L., LAMBON-RALPH.,M., (2019) Exploring distinct default mode and semantic networks using a systematic ICA approach., Cortex, 113: 279–297. [Open Access]

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