John Suckling

Prof John Suckling is Director of Research in Psychiatric Neuroimaging in the Department of Psychiatry. A physicist with over 30 years’ experience in medical imaging, he applies neuroimaging and biostatistics to investigate diverse neurodevelopmental and psychopathological conditions and their treatment through neuroscience, experimental medicine, and large-scale multi-centre clinical trials. Prof Suckling also has a strong interest in research ethics and is the Chair of the Psychology Research Ethics Committee, and the University Research Ethics Committee that oversees University policy for research that involves human participants and personal data. Together with colleagues from School of Arts and Humanities and School of Technology he is leading a new interdisciplinary Centre for Human Inspired Artificial Intelligence (CHIA) with the goal of advancing Artificial Intelligence for the benefit of humanity. Neuroimaging is a major contributor to the renaissance of experimental psychiatry and psychology. Drawing on the extensive infrastructure of Cambridge Neuroscience and Department of Psychiatry’s close links between research and clinical practice, we have a programme of large-scale, multi-centre clinical studies using neuroimaging as the primary measure to better understand the neurobiology of mental health disorders as well as measuring the efficacy of possible treatments. These studies, and others, are integrated into our expanding data capture, archive and access system that records both study variables and operational details. 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.