Total Body PET 2021 conference [rescheduled] Sep 22, 2021 - Sep 24, 2021 — Virtual Meeting (online)
PET is Wonderful Annual Meeting 2021 Oct 26, 2021 12:00 AM — Virtual Meeting (online)
NRS Mental Health Network Annual Scientific Meeting 2021 Nov 02, 2021 09:00 AM - 05:00 PM — Royal College of Physicians, Edinburgh (and online)


SINAPSE experts from around Scotland have developed ten online modules designed to explain medical imaging. They are freely available and are intended for non-specialists.

Edinburgh Imaging Academy at the University of Edinburgh offers the following online programmes through a virtual learning environment:

Neuroimaging for Research MSc/Dip/Cert

Imaging MSc/Dip/Cert

PET-MR Principles & Applications Cert

Applied Medical Image Analysis Cert

Online Short Courses

First SINAPSE seed fund PhD studentship available at Aberdeen

Prediction Models of Dementia Risk from Brain MR Images and Proxies of Cognitive Reserve

SINAPSE has awarded funding for five PhD studentships on imaging-related research projects in partnership with external organisations.  The first studentship announced is at the University of Aberdeen, in collaboration with the University of Edinburgh and industry partner IXICO.

Project summary:

Dementia is a global public health problem, the importance of which cannot be underestimated. Brain imaging is recommended by UK and international guidelines in the assessment of all patients as part of the diagnostic work up for dementia, both to try to diagnose the likely underlying disease causing dementia, (such as shrinkage of certain regions due to Alzheimer’s pathology and/or evidence of blood vessel disease) and to exclude a potentially surgically treatable abnormality such as a large bleed or tumour, which occurs in a very small proportion of patients. However, at present we cannot diagnose dementia from a scan because of individual differences in how people cope with brain diseases, with some able to overcome disease for some time before developing clinically obvious dementia. Because of these individual differences, often due to variation in brain experience during life, due to education, literacy, occupation, at present we cannot diagnose severity of cognitive impairment or dementia from a scan.

The purpose of this project is to identify the most important life-course factors that influence differences in reserve/resilience and include these in new mathematical methods of analysis of brain magnetic resonance images (MRI) to develop models that can accurately predict dementia risk from a brain scan.

Such “machine learning models” may be able to quantify the disconnect between the amount of disease present (MRI imaging) and cognitive symptoms and relate it to life course determinants. If these models can be developed and are accurate in predicting how impaired a patient is and what the likely underlying pathology is, these approaches will be extremely useful both for clinical trials of new drugs and for early diagnosis of patients with dementia in the NHS. These models could be incorporated into the computer work stations used for routine NHS brain scan reporting, improving accuracy of diagnosis and ultimately improving patient care by allowing early diagnosis, appropriate intervention and care.

Application deadline: 27th February 2016

For more information on the project and how to apply, please visit:

Details of the other awarded seed fund studentships to follow: PhD Opportunities.