Alzheimer’s disease (AD) is the most common cause of dementia and one of the main health problems in the elderly worldwide. The estimated worldwide cost of caring for 47M affected by dementia was US$818 billion in 2015 and UK is expected to have 1M cases by 2021 . Mild cognitive impairment (MCI) is usually considered as an intermediate stage between the cognitive declines associated with normal aging and a state of dementia . To address the challenge of AD, several worldwide ageing studies (cf. ) are being undertaken.
These studies often include multiple brain imaging modalities such as EEG, MEG, PET, and MRI. In particular, MEG is a technique specifically designed to measure dynamic neural activity non-invasively featuring very high time and spatial resolution, and has been increasingly applied in the study of MCI and AD. Recent studies based on MEG have also demonstrated that pharmacological treatment for early AD and MCI can slow the progression of the disease . As part of NI Functional Brain Mapping (FBM) facility, an MEG-based brain connectivity study is underway with the objective of characterizing MCI, which is crucial for early detection of progression from MCI to AD. In addition, our recent EU funded project on redesigning dementia care pathway will involve large heterogeneous data.
Working along with these major funded projects, this project will involve performing comprehensive data analysis on multi-modality neuroimaging data to discover stratified neuromarkers for early prediction of an individual’s possible progression to AD.
The PhD researcher will first undertake a thorough review of the AD literature, particularly related with structural and functional connectivity changes in cognitively impaired brain. Next the student will seek to gain access to available multi-modal neuroimaging data and undertake appropriate pre-processing and analysis of the data to attain a deeper insight. This will be followed by a detailed investigation into a range of feature extraction and selection procedures, and machine learning algorithms, so as to identify robust changes in brain patterns related with neuronal connectivity and/or oscillations in the brains of a large population of healthy persons, people with MCI, and AD patients.
Anticipated Outcomes: The neuromarkers identified in the project will have strong potential for inclusion in a clinical procedure that enables clinicians to routinely use MEG and other neuroimaging data in the assessment of individuals presenting with symptoms consistent with early stages of dementia type impairments.
1.Prince et al. (2015). World Alzheimer Report 2015, pp 1-21.
2.Petersen et al. (2009). Early diagnosis of Alzheimer’s disease: Is MCI too late? Curr. Alzheimer Res. 6:324–30.
3.Feldman et al. (2005). Mild cognitive impairment. Am J Geriatr Psychiatry.13(8):645-55.
4.Cambridge Centre for Ageing and Neuroscience: http://www.cam-can.org/.
5.Youssofzadeh et al. (2015). Multi-kernel learning with Dartel improves combined MRI-PET classification of Alzheimer’s disease in AIBL data: Group and Individual Analyses. Front. Hum. Neurosci., 11:380.
6.Youssofzadeh et al. (2016). Temporal information of directed causal connectivity in multi-trial ERP data using partial Granger causality. Neuroinformatics, 14(1):99-120.
- To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.
- Experience using research methods or other approaches relevant to the subject domain
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
- First Class Honours (1st) Degree
- Masters at 70%
- For VCRS Awards, Masters at 75%
- Publications - peer-reviewed
The University offers the following awards to support PhD study and applications are invited from UK, EU and overseas for the following levels of support:
Vice Chancellors Research Studentship (VCRS)
Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £15,000 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Vice-Chancellor’s Research Bursary (VCRB)
Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £7,500 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Vice-Chancellor’s Research Fees Bursary (VCRFB)
Fees only award (PhD fees + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Department for the Economy (DFE)
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £15,285 per annum for three years. EU applicants will only be eligible for the fee’s component of the studentship (no maintenance award is provided). For Non-EU nationals the candidate must be "settled" in the UK. This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Due consideration should be given to financing your studies; for further information on cost of living etc. please refer to: www.ulster.ac.uk/doctoralcollege/postgraduate-research/fees-and-funding/financing-your-studies
- Computing, Engineering and the Built Environment
- School of Computing, Engineering and Intelligent Systems
The Doctoral College at Ulster University
As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day
Adrian Johnston - PhD in InformaticsWatch Video