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About

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.

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Skills

Experience

Director of Research in Psychiatric Neuroimaging

University of Cambridge

Oct-2010 to Present

Publication

  • dott image November, 2024

An explainable three dimension framework to uncover learning patterns: A unified look in variable sulci recognition

The significant features identified in a representative subset of the dataset during the learning process of an artificial intelligence model are referred to as a 'global' explanation. 3D gl...

  • dott image May, 2024

The Explanation Necessity for Healthcare AI

Explainability is often critical to the acceptable implementation of artificial intelligence (AI). Nowhere is this more important than healthcare where decision-making directly impacts patie...

  • dott image May, 2024

Contrastive-Adversarial and Diffusion: Exploring pre-training and fine-tuning strategies for sulcal identification

Journal : arxiv Electrical Engineering and Systems Science

In the last decade, computer vision has witnessed the establishment of various training and learning approaches. Techniques like adversarial learning, contrastive learning, diffusion denoisi...

  • dott image May, 2024

Solving the enigma: Deriving optimal explanations of deep networks

The accelerated progress of artificial intelligence (AI) has popularized deep learning models across domains, yet their inherent opacity poses challenges, notably in critical fields like hea...

  • dott image August, 2019

Minocycline for negative symptoms of schizophrenia and possible mechanistic actions: the BeneMin RCT

Journal : Efficacy and Mechanism Evaluation

Background In a previous trial we reported that the neuroprotective, anti-inflammatory antibiotic minocycline lessened the negative symptoms of schizophrenia compared with placebo over 1 ye...

  • dott image November, 2018

The benefit of minocycline on negative symptoms of schizophrenia in patients with recent-onset psychosis (BeneMin): a randomised, double-blind, placeb...

Background The antibiotic minocycline has neuroprotective and anti-inflammatory properties that could prevent or reverse progressive neuropathic changes implicated in recent-onset schizophr...

  • dott image April, 2018

T86. COGNITIVE SUBTYPES IN FIRST-EPISODE PSYCHOSIS: AN EMPIRICAL LONGITUDINAL STUDY OF RELATIONSHIP TO COGNITIVE, SYMPTOM AND FUNCTIONAL OUTCOMES

Background Variable outcomes following a first-episode of psychosis are partly attributable to heterogeneity in cognitive functioning. Previous work in first episode psychosis has identifie...

  • dott image May, 2017

373. Adolescence is Associated with Genomically Patterned Consolidation of the Hubs of the Human Brain Connectome

Adolescence is a period of human brain growth and high incidence of mental health disorders. The Neuroscience in Psychiatry Network seeks to understand biological underpinnings of the adoles...

  • dott image February, 2016

Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome

Journal : Proceedings of the National Academy of Sciences 1091-6490

How does human brain structure mature during adolescence? We used MRI to measure cortical thickness and intracortical myelination in 297 population volunteers aged 14–24 y old. We found an...

  • dott image February, 2016

Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome

Adolescence is a period of human brain growth and high incidence of mental health disorders. Here, we show consistently in two MRI cohorts that human brain changes in adolescence were concen...