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Imaging-based neurophenotypes to model developmental mental health disorders


Project Description

Mental health disorders such as attention deficit hyperactivity disorder (ADHD) and autism spectrum disorders (ASD) arise during early development [1], even though they might be diagnosed only later in life when treatment becomes less effective. Neuroimaging offers the unique capability of providing connectivity profiles non-invasively and in vivo, potentially for large numbers of subjects of any age. Therefore,
extracting and selecting clinically-relevant features from neuroimaging data using machine learning represents a necessary step for probing the aetiology, differentiating the sub-type and designing the best treatment for several developmental mental disorders. This project aims to develop and use multi-modal (i.e., structural and functional) neuroimaging methods to extract accurate markers [2] for improving personalised diagnosis and predicting treatment outcome in patients with developmental mental health disorders, such as ADHD and ASD. The student will apply and expand state-of-the-art analysis methods to extract the maximum information from the available multicentre big cohort imaging studies that include clinical information [3, 4]. The analysis pipelines will need to be highly optimised and run on high performance computing (HPC) solutions. Moreover, linking abnormal brain development and clinical outcomes is a challenging task. Generalizability of the discovered imaging-based neurophenotypes will need to be assessed using the available big datasets and sophisticated cross-validation/replication frameworks. The analysis of such datasets will require the development of data reduction, data-driven exploratory and statistical approaches to define biologically relevant and interpretable imaging-derived-phenotypes.

The student will take advantage of the world-class interdisciplinary research environment at the Sir Peter Mansfield Imaging Centre, University of Nottingham. Being at the interface between biomedical engineering research and clinical applications, the project will greatly benefit from the interdisciplinary expertise available in Nottingham, including membership of the Institute of Mental Health, a collaboration between the University of Nottingham and Nottinghamshire Healthcare NHS Trust. Moreover, the Nottingham Biomedical Research Centre has a theme on mental health, allowing evaluation of efficacy of the proposed technology to available clinical cohorts, opening the path for clinical translation.

Funding Notes

Deadline for applications is 25 February 2019, with interviews for applicants to take place between 4 and 8 March 2019
Applicants for the Precision Imaging PhD programme should have at least a 2:1 degree, or equivalent, in a project-relevant discipline. Funding is only available for UK and EU students.

References

References
1. Marin, O., Developmental timing and critical windows for the treatment of psychiatric disorders. Nat Med, 2016. 22(11): p.1229-1238.
2. Drysdale, A.T., et al., Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med, 2017. 23(1): p. 28-38.
3. Consortium, H.D., The ADHD-200 Consortium: A Model to Advance the Translational Potential of Neuroimaging in Clinical Neuroscience. Front Syst Neurosci, 2012. 6: p. 62.
4. Di Martino, A., et al., Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Sci Data, 2017. 4: p. 170010.

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