Translating brain magnetic resonance imaging signals to iron and myelin to appraise Alzheimer's disease

Aim of the PhD Project:

To determine the individual and combined contributions of iron and myelin to multi-modality quantitative magnetic resonance imaging (QMRI) signals.

Project Description

Iron accumulates in the brain with ageing, with advancing age being the major risk factor for neurodegenerative diseases such as Alzheimer's Disease (AD; ). Indeed, iron dyshomeostasis is a feature of AD [3] and iron chelation therapy is undergoing a clinical trial for AD.

Quantitative magnetic resonance imaging MRI (QMRI) methods are sensitive to iron content (to differing extents and specificities). The So Lab and others have shown T2* values compared to T1 and T2, correlated best with iron. Unusually, the So Lab has correlated QMRI data with quantitative spatial iron measurements obtained by synchrotron radiation X-ray fluorescence (SRXRF), rather than rely on quantitative bulk iron analyses or non-quantitative histochemical iron staining.

Myelin is also known to significantly modulate QMRI signals and the situation is further complicated by the high iron content of myelin itself. Myelin is formed from the wrapping of oligodendrocyte membranes around axons and functions as "electrical insulation" to ensure fast nerve conduction. Conventionally, myelin is assessed by (immuno)histochemical staining and qualitative, rather than quantitative.

Generally, relationships between QMRI with iron and/or myelin are evaluated by ex vivo QMRI of brain samples and then sectioning of the sample for correlative iron and/or myelin histology as mentioned above. Disparate resolutions between such diverse data types and artefacts from histological processing contributes to inaccuracies/ambiguities, especially when co-registering datasets. Using bespoke high signal-to-noise MRI coils (previously developed by the So lab with PulseTeq Ltd), thin sections of brain tissues will undergo high resolution QMRI prior to quantitative iron and myelin mapping by SRXRF/laser-ablation-inductive coupled plasma-mass spectrometry (LA-ICP-MS) and desorption electrospray ionisation-mass spectroscopic (DESI-MSI)/Raman imaging, respectively. Uniquely, multi-modality imaging will be performed at comparable resolutions with minimal sample displacement between modalities. High resolution QMRI of thin brain samples is challenging but made possible using bespoke MRI coils. In this manner, registration errors between datasets are minimised and determination of accurate pixel-wise relationships between individual QMRI, iron and myelin imaging datasets are possible. Notably, lipid composition and myelin structural information can also be obtained to determine relationships between QMRI and specific lipid types/myelin structure.

In this project, we aim to correlate iron- and myelin-sensitive QMRI signals with quantitative state-of-the-art physiochemical iron and myelin mapping developed by So and Bergholt Labs, respectively. Age-matched control and AD brain samples will be obtained from the Brain Bank for Dementia (which Dr So has previously accessed) and analysed, for potential future translation to monitoring iron chelation therapies in man. While AD is often considered a grey matter disease, white matter myelin has also been shown to be deranged. Teasing apart contributions of myelin and iron to multimodality QMRI measurements aids true assessment of the roles of iron dyshomeostasis and myelin dysfunction in AD for identification of novel AD therapeutics and monitoring.

Strains on the healthcare system in the UK create an acute need for finding more effective, efficient, safe, and accurate non-invasive imaging solutions for clinical decision-making, both in terms of diagnosis and prognosis, and to reduce unnecessary treatment procedures and associated costs. Medical imaging is currently undergoing a step-change facilitated through the advent of artificial intelligence (AI) techniques, in particular deep learning and statistical machine learning, the development of targeted molecular imaging probes and novel "push-button" imaging techniques. There is also the availability of low-cost imaging solutions, creating unique opportunities to improve sensitivity and specificity of treatment options leading to better patient outcome, improved clinical workflow and healthcare economics. However, a skills gap exists between these disciplines which this CDT is aiming to fill.

Consistent with our vision for the CDT in Smart Medical Imaging to train the next generation of medical imaging scientists, we will engage with the key beneficiaries of the CDT: (1) PhD students & their supervisors; (2) patient groups & their carers; (3) clinicians & healthcare providers; (4) healthcare industries; and (5) the general public. We have identified the following areas of impact resulting from the operation of the CDT.

- Academic Impact: The proposed multidisciplinary training and skills development are designed to lead to an appreciation of clinical translation of technology and generating pathways to impact in the healthcare system. Impact will be measured in terms of our students' generation of knowledge, such as their research outputs, conference presentations, awards, software, patents, as well as successful career destinations to a wide range of sectors; as well as newly stimulated academic collaborations, and the positive effect these will have on their supervisors, their career progression and added value to their research group, and the universities as a whole in attracting new academic talent at all career levels.

- Economic Impact: Our students will have high employability in a wide range of sectors thanks to their broad interdisciplinary training, transferable skills sets and exposure to industry, international labs, and the hospital environment. Healthcare providers (e.g. the NHS) will gain access to new technologies that are more precise and cost-efficient, reducing patient treatment and monitoring costs. Relevant healthcare industries (from major companies to SMEs) will benefit and ultimately profit from collaborative research with high emphasis on clinical translation and validation, and from a unique cohort of newly skilled and multidisciplinary researchers who value and understand the role of industry in developing and applying novel imaging technologies to the entire patient pathway.

- Societal Impact: Patients and their professional carers will be the ultimate beneficiaries of the new imaging technologies created by our students, and by the emerging cohort of graduated medical imaging scientists and engineers who will have a strong emphasis on patient healthcare. This will have significant societal impact in terms of health and quality of life. Clinicians will benefit from new technologies aimed at enabling more robust, accurate, and precise diagnoses, treatment and follow-up monitoring. The general public will benefit from learning about new, cutting-edge medical imaging technology, and new talent will be drawn into STEM(M) professions as a consequence, further filling the current skills gap between healthcare provision and engineering.

We have developed detailed pathways to impact activities, coordinated by a dedicated Impact & Engagement Manager, that include impact training provision, translational activities with clinicians and patient groups, industry cooperation and entrepreneurship training, international collaboration and networks, and engagement with the General Public.