Mr
Shaun R.
Stone
Position:
PhD Candidate - Medical Imaging
Interests:
Shaun has recently started his PhD studentship in Medical Imaging at the University of Aberdeen. His project is titled, “Prediction Models of Dementia Risk from Brain MR Images and Proxies of Cognitive Reserve” with Professor Alison D. Murray (University of Aberdeen), Dr Roger Staff (NHS), Professor Joanna Wardlaw, Professor Craig Ritchie (University of Edinburgh) and Dr Robin Wolz (IXICO). His project is funded by SINAPSE and industry partners IXICO. Shaun completed his undergraduate (BSc) degree in Psychology with Neuroscience, and postgraduate (MSc) degree in Neuroimaging at Bangor University, North Wales - where he gained experience using a range of neuroimaging techniques (namely, fMRI). Shaun continues to learn, using his neuro-background to transfer into a medical and diagnostic imaging domain and is looking forward to his future career in research or industry, thanks to this opportunity.
Institute: Aberdeen Biomedical Imaging Centre (ABIC)
Department:
School of Medicine
Keywords:
Ageing
,
Alzheimer's
,
Cerebral atrophy
,
Cognitive Control
,
Database
,
Dementia
,
Diffusion imaging
,
DTI-tractography
,
Executive Function
,
Functional Connectivity
,
Functional MRI (fMRI)
,
Image analysis
,
Image processing
,
Leukoaraiosis
,
MRI hardware
,
MRS
,
Neuroradiology
,
Radiology
,
Statistics
,
Structural imaging
,
Translational Imaging
,
White matter disease
Research Themes
The project aims to identify the most important life-course factors and brain biomarkers that influence differences in cognitive reserve and cognitive resilience. These proxies of cognitive decline will then be incorporated into machine learning algorithms for automatic detection and classification of dementia risk from a brain scan. Such machine learning models may be able to quantify the discrepancy between the amount of disease present (MR images) and cognitive symptoms and relate this to life-course determinants. That is, given the life-course of an individual, what are the imaging characteristics that allow us to predict cognitive decline and therefore future dementia risk? Providing these models are accurate in predicting patient pathology, these approaches will be extremely useful for both clinical/pharmaceutical trials and for earlier diagnosis of patients with dementia in the NHS - improving the accuracy of diagnosis and more importantly earlier diagnosis for more appropriate intervention and care.
Collaborators
IXICO: Innovative Technologies for Treating Serious Diseases
Software Expertise
IBM's SPSS Statistics Software
MathWork's MATLAB
RStudio
FMRIB Software Library (FSL)
Statistical Parametric Mapping (SPM)
