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Funded PhD Opportunity

Large-Scale Multimodal Brain Connectivity Analysis for Discovering Neuromarkers for Early Detection of Alzheimer’s Disease

Subject: Computer Science and Informatics


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 [1]. Mild cognitive impairment (MCI) is usually considered as an intermediate stage between the cognitive declines associated with normal aging and a state of dementia [2]. To address the challenge of AD, several worldwide ageing studies (cf. [4]) 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 [3]. 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:

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.

Essential criteria

  • Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC)
  • Experience using research methods or other approaches relevant to the subject domain

Desirable Criteria

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


    Vice Chancellors Research Scholarships (VCRS)

    The scholarships will cover tuition fees and a maintenance award of £14,777 per annum for three years (subject to satisfactory academic performance). Applications are invited from UK, European Union and overseas students.


    The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 14,777 per annum for three years. EU applicants will only be eligible for the fees component of the studentship (no maintenance award is provided).  For Non EU nationals the candidate must be "settled" in the UK.

Other information

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 Informatics

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Key dates

Submission deadline
Monday 19 February 2018

Interview Date
12 March 2018

Contact supervisor

Professor Girijesh Prasad

Other supervisors


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