Skip to content

Integrative neuromodulation therapy for depression S&T35

  • School: School of Science and Technology
  • Study mode(s): Full-time / Part-time
  • Starting: 2022
  • Funding: UK student / EU student (non-UK) / International student (non-EU) / Fully-funded


NTU's Fully-funded PhD Studentship Scheme 2022

Project ID: S&T35

Mood disorders, including depression are the leading cause of disability worldwide. Pharmaceutical interventions for depression are only effective in 33% of, predominantly in more severe cases. There is a desperate need to develop more effective diagnostic and intervention tools for mild-moderate cases. Neuromodulation is a type of operant conditioning used to alter cognitive and emotional processes through guided regulation of brain and body function. The proposed project seeks to integrate several brain and body signals (brain waves, cerebral oxygenation changes, cardiac signals) to 1) understand and categorise negative mood states (depression, anxiety), in relation to emotional and cognitive functions implicated in predisposition to such states; 2) develop multimodal neuromodulation as an intervention for mood disorders; 3) apply machine learning methods to classify biological states (subtypes of depression and anxiety) and integrate multiple modalities for neuromodulation therapy.

The work will include 1) the application of machine learning methods to self-report questionnaire and brain function data in order to identify novel targets for neuromodulation; 2) development of neuromodulation software and its integration with existing equipment to assess brain function and 3) running initial investigations to evaluate the novel neuromodulation intervention. The outputs are expected to include innovative perspectives on functional brain correlates of subtypes of depression, and the effects of prefrontal cortical and cardiac feedback training on sustained changes in self-reported depression.

The student will become part of a dynamic team of researchers at NTU, leveraging existing state-of-the-art hardware, software and data to develop novel intervention directions for depression. They will capitalise on the combined expertise of the supervisory team in the fields of neuroimaging, machine learning, mental state decoding, medical devices, biopsychology and interventions for mental health and mood disorders.

This work will foster long-term, interdisciplinary research collaboration at NTU with the goal of developing advanced neuromodulation therapies to improve wellbeing and reduce risk for psychological illness. The proposed study represents a low risk and potentially very high return on investment. By fostering this interdisciplinary collaboration, NTU will pave the way for a longer-term stream of high-quality outputs, grant income, engineer-psychologist-physician collaborations and commercial applications, which extend well beyond this specific project.

The candidate we seek will be a creative and rigorous and evaluative thinker with strong programming experience (Matlab preferred but not required), and have a background in some of the following: signal analysis, neuroscience and/or psychology, human subjects research. We are particularly interested in working with people who share a common enthusiasm and motivation for helping to improve psychological health through biomedical innovation.

School strategic research priority

The proposed studentship project aligns with the Health and Wellbeing and Medical Technologies and Advanced Materials (MTAM) research themes and it also aligns with Imaging, Materials and Engineering Centre (IMEC).

Entry qualifications

For the eligibility criteria, visit our studentship application page.

How to apply

For guidance and to make an application, please visit our studentship application page. The application deadline is Friday 14 January 2022.

Fees and funding

This is part of NTU's 2022 fully-funded PhD Studentship Scheme.

Guidance and support

Download our full applicant guidance notes for more information.

Still need help?

+44 (0)115 941 8418