Mark Jenkinson
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Professor of Neuroimaging
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Mark Jenkinson is a Professor of Neuroimaging at the University of Adelaide and the South Australian Health and Medical Research Institute (SAHMRI) in Australia. His research areas include neuroimaging techniques in the study of neurological disorders, particularly focusing on Parkinson’s and Huntington’s diseases, as well as machine learning applications in medical image segmentation. Recent articles authored by Professor Jenkinson explore topics such as cognitive and affective changes in neurodegenerative conditions, automated lesion segmentation in multiple sclerosis, and the challenges of translating big data imaging studies into clinical practice.
Recent Grants
Grant: Close
Mapping the Human Connectome: Structure, Function and Heritability
Open Date: 2010-07-01
Close Date: 2015-06-01
Grant: Close
New Tools for Understanding White Matter Disease using Diffusion MRI
Open Date: 2009-09-01
Close Date: 2012-09-01
Grant: Close
Integrated structural and diffusion imaging for Alzheimer's
Open Date: 2006-07-01
Close Date: 2008-07-01
Grant: Close
Application and Development of an FMRI Simulator for Investigation of Structure-Function Relations in the Brain
Open Date: 2005-10-01
Close Date: 2010-09-01
Grant: Close
Functional brain imaging: Relating patient data to the population
Open Date: 2005-08-01
Close Date: 2007-08-01
Articles (12)
Year:
2025
Neuroimaging involves the acquisition of extensive 3D images and 4D time series data to gain insights into brain structure and function. The analysis of such data necessitates both spatial and temporal processing. In this context, “fslmaths” has established itself as a foundational software tool within our field, facilitating domain-specific image processing. Here, we introduce “niimath,” a clone of fslmaths. While the term “clone” often carries negative connotations, we illustrate the merits of replicating widely-used tools, touching on aspects of licensing, performance optimization, and portability. For instance, our work enables the popular functions of fslmaths to be disseminated in various forms, such as a high-performance compiled R package known as “imbibe”, a Windows executable, and a WebAssembly plugin compatible with JavaScript. This versatility is demonstrated through our NiiVue live demo web page. This application allows ‘edge computing’ where image processing can be done with a zero-footprint tool that runs on any web device without requiring private data to be shared to the cloud. Furthermore, our efforts have contributed back to FSL, which has integrated the optimizations that we’ve developed. This synergy has enhanced the overall transparency, utility and efficiency of tools widely relied upon in the neuroimaging community.
Year:
2024
Year:
2024
Year:
2023
Year:
2023
Huntington’s and Parkinson’s disease are two movement disorders representing mainly opposite states of the basal ganglia inhibitory function. Despite being an integral part of the cortico-subcortico-cortical circuitry, the subthalamic nucleus function has been studied at the level of detail required to isolate its signal only through invasive studies in Huntington’s and Parkinson’s disease. Here, we tested whether the subthalamic nucleus exhibited opposite functional signatures in early Huntington’s and Parkinson’s disease. We included both movement disorders in the same whole-brain imaging study, and leveraged ultra-high-field 7T MRI to achieve the very fine resolution needed to investigate the smallest of the basal ganglia nuclei. Eleven of the 12 Huntington’s disease carriers were recruited at a premanifest stage, while 16 of the 18 Parkinson’s disease patients only exhibited unilateral motor symptoms (15 were at Stage I of Hoehn and Yahr off medication). Our group comparison interaction analyses, including 24 healthy controls, revealed a differential effect of Huntington’s and Parkinson’s disease on the functional connectivity at rest of the subthalamic nucleus within the sensorimotor network, i.e. an opposite effect compared with their respective age-matched healthy control groups. This differential impact in the subthalamic nucleus included an area precisely corresponding to the deep brain stimulation ‘sweet spot’—the area with maximum overall efficacy—in Parkinson’s disease. Importantly, the severity of deviation away from controls’ resting-state values in the subthalamic nucleus was associated with the severity of motor and cognitive symptoms in both diseases, despite functional connectivity going in opposite directions in each disorder. We also observed an altered, opposite impact of Huntington’s and Parkinson’s disease on functional connectivity within the sensorimotor cortex, once again with relevant associations with clinical symptoms. The high resolution offered by the 7T scanner has thus made it possible to explore the complex interplay between the disease effects and their contribution on the subthalamic nucleus, and sensorimotor cortex. Taken altogether, these findings reveal for the first time non-invasively in humans a differential, clinically meaningful impact of the pathophysiological process of these two movement disorders on the overall sensorimotor functional connection of the subthalamic nucleus and sensorimotor cortex.
Year:
2023
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2022
Year:
2022
Year:
2022
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2021
Year:
2020
Year:
2020
Collaborators (15)
Marco Battaglini
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Ana Namburete
Research Lecturer in AI and Health Data Science
University of Oxford
David Abbott
Head, Neuroinformatics
Florey Institute of Neuroscience and Mental Health
Chris Patrick Pflanz
University of Oxford
Marten F. Snel
University of Adelaide
Eva Bezak
Professor at University of South Australia
University of South Australia
Gwenaëlle Douaud
University of Oxford
Ludovica Griffanti
University of Oxford
Graeme Jackson
Florey Institute of Neuroscience and Mental Health
Kristine Krug
Heisenberg-Professor; Chair in Sensory Physiology
Otto-von-Guericke Universität Magdeburg
Saad Jbabdi
Associate Professor
University of Oxford
Gregory Brown
Researcher / lecturer
University of South Australia
Stephan Lau
University of Adelaide
Madeleine K Wyburd
Københavns Universitet
Taylor Hanayik
University of Oxford
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