Dr John Suckling

University of Cambridge

NHS or other affiliations
Cambridgeshire and Peterborough Foundation NHS Trust

Position: Professor
Personal home page: https://www.neuroscience.cam.ac.uk/directory/profile.php?js369

PubMed journal articles - click here

Dr John Suckling is pleased to consider applications from prospective PhD students.

Research description

Our research programme leverages two decades of advancements in magnetic resonance imaging and assocaited methodologies to accrue the evidence to inform the difficult discussions with patients with brain tumours, and their families, on the balance between extending life and preserving cognition; a very personal decison. 

Although conventional MRI is a fundamental clinical tool for brain tumour diagnosis and monitoring, the spatially extended topography of brain networks sub-serving cognition makes predicting functional impairments challenging. We have previously shown that focal brain tumours produce long-range gradients in function, and consequently that their effects require interpretation in terms of changes in functional network architecture. We have also discovered that the spatial distribution of brain tumours is largely explained by brain regions that are the connections between networks that are highly metabolically active, express genes for metabolic processes, cell division and gliomagenesis, and are co-located with progenitor cells, and that  increasing space occupancy of tumours exerts a detrimental effect on memory following treatment by its perturbation of the associated functional network. Together, this evidence leads us to believe that the type and severity of tumour- and treatment-induced cognitive deficits is dependent on which network, and specifically which components of the network, interact with the tumour.


Research Programme or Virtual Institute
Brain Cancer Virtual Institute
Secondary Programme
Advanced Cancer Imaging
Methods and technologies
Clinical trials
Magnetic Resonance Imaging (MRI)
Statistical analysis
Tumour type interests
Brain and central nervous system
Brain tumour; glioma; magnetic resonance imaging
machine learning
Recent publications:
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Key publications
  1. Romero-Garcia R, Erez Y, Oliver G, Owen M, Merali S, Poologaindran A, Morris RC, Price SJ, Santarius T, Suckling J, Hart MG (2020). Practical application of networks in neurosurgery: combined 3D printing, neuronavigation and pre-operative surgical planning. World Neurosurg. S1878-8750(20)30103-0.
  2. Hart MG, Romero-Garcia R, Price SJ, Suckling J (2018). Global Effects of Focal Brain Tumors on Functional Complexity and Network Robustness: A Prospective Cohort Study. Neurosurgery 84: 1201-1213
  3. Hart MG, Price SJ, Suckling J (2016). Connectome analysis for pre-operative brain mapping in neurosurgery. Br J Neurosurg. 30: 506-517. 
Multiple linear regression model relating connectomic, cellular, and transcriptomic factors with glioma distribution. A. Schematic of the multiple linear regression model. Intercept term and error term are not displayed. B. Fitted values and residuals of glioma distribution model. C. Scatter plot of predicted versus observed tumor frequency values. D. Percent of variance explained by each individual predictor of tumor frequency. These values were calculated using the partial correlation coefficient between each measure and tumor frequency.